Towards Verifiable Text Generation with Symbolic References
Authors: Lucas Torroba Hennigen
Zejiang Shen
Aniruddha Nrusimha
Bernhard Gapp
David Sontag
Yoon Kim
Venue: COLM (2024)
Keywords: attribution, verification of llm generations, symbolic linking, placeholder, template generation
TL;DR: SymGen is a method that prompts an LLM to interleave its regular output text with explicit symbolic references to fields in the source data; it enables easier human verification of the generation.

Abstract: LLMs are vulnerable to hallucinations, and thus their outputs generally require laborious human verification for high-stakes applications. To this end, we propose symbolically grounded generation (SymGen) as a simple approach for enabling easier manual validation of an LLM’s output. SymGen prompts an LLM to interleave its regular output text with explicit symbolic references to fields present in some conditioning data (e.g., a table in JSON format). The references can be used to display the provenance of different spans of text in the generation, reducing the effort required for manual verification. Across a range of data-to-text and question-answering exper- iments, we find that LLMs are able to directly output text that makes use of accurate symbolic references while maintaining fluency and factuality. In a human study we further find that such annotations can streamline human verification of machine-generated text.

Published as a conference paper at COLM 2024
Towards verifiable text generation with symbolic references
Lucas Torroba Hennigen S*
Shannon Zejiang Shen
Aniruddha Nrusimha
Bernhard Gapp* David Sontag Yoon Kim
"Massachusetts Institute of Technology *Good Data Initiative
Lucastor@mit.edn z zjshen@mit t. edu anin@mit t. edu
bernhard.gaapeeoooon wwwpeeoodddddaaaaaaaaaaacoooooea com
dsontagemit edu
yoonkimêmi edu
Abstract
LLMs are vulnerable to hallucinations, and thus their outputs generally require laborious human verification for high-stakes applications. To this end, we propose symbolically grounded generation (SymGen) as simple ap- proach for enabling easier manual validation of an LLM's output. SymGen prompts an LLM to interleave its regular output text with explicit symbolic references to fields present in some conditioning data (e.g., table in JSON format). The references can be used to display the provenance different spans of text in the generation, reducing the effort required for manual verification. Across a range of data-to-text and question-answering exper- iments, find that LLMs are able to directly output text that makes use accurate symbolic references while maintaining fluency and factuality. In a human study we further find that such annotations can streamline human verification of machine-generated text.
https://symgen. http:///mmge..ggh........ io
1 Introduction
Many applications of generative AI involve generating text based on structured data (e.g. tabular data, surveys, API outputs, etc.) that is known (or assumed) to be trustworthy. For example, newspapers may be interested in generating summaries of sports games based on official match statistics, and search providers would want generative search engines to ground its output in search results. These applications require conditional text generation that is fluent, accurate, and verifiable with respect to the conditioning information. Large language models (LLMs) have advanced to the point where they sometimes be relied upon to generate fluent- and faithful-enough summaries of text and other structured data. However they remain far from perfect (Liu et al., 2023 Yue al., 2023), and insofar as high-stakes applications will almost certainly require human to manually verify that an LLM's generation accurate with respect to its conditioning information, there is need to develop frameworks that maintain the fluent and accurate few-shot generation capabilities of LLMs while enabling streamlined human verification of the model's output.
This paper proposes symbolically grounded generation (SymGen) as step towards verifi- able text generation with I.MMs. Given a string representation of data in a structured but human-readable format (e.g., JSON, YAML, etc.), we prompt the LLM to generate the out- put with explicit symbolic references into the provided data structure instead of generating the text directly. The references are then rendered using parser, which can faithfully rep- resent values from the original data, and can moreover be used to include visual cues that enable user to readily verify the provenance of a particular span of text (see Fig. 2 for overview). In contrast to classic templated approaches for text generation (Kukich 1983: McKeown, 1992: McRoy et al. 2000), SymGen instead offloads the template specification process to an LLM We exploit the fact that LLMs have likely been sufficiently exposed to such kind of templated text during pretraining that they are able perform zero- and few- shot text generation with symbolic references across multiple domains.
Equal contribution.

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Published as a conference paper at COLM 2024
To demonstrate its effectiveness, evaluate SymGen on its ability to retain the same tex- tual quality as regular generation while improving the ability of users to verify the final LLM response. Specifically, we study SymGen's performance along four axes and across five datasets: textual quality ($3) citation accuracy ($4.1), helpfulness of SymGen citations ($/4.), and math- ematical reasoning ( Overall, our experiments on zero- and few-shot data-to-text gen- eration and question answering indicate that LLMs can be prompted to generate fluent text that is interleaved with symbolic references which explicitly refer to data fields. Further, we find that the annotations enabled by SymGen simplify the process of post-hoc verification, both as perceived by the user and by reducing the average verification time by 20%.
and effectiveness in downstream extensions of SymGen to question-answert
2 Text generation with symbolic references
Task formulation. Let V be some vocab- ulary, and let F be a set of fields in a data structure. One special case of conditional generation consists of taking as input some structured data encoded as field-value tu- ples {(fr D=))"= with field f; E F, value d; E V', and some instruction e v", and then producing an appropriate re- sponse y = [w.,..., Wn] V*. One sim- ple technique for this task is to encode the
Game data
Ceneration without referenceerr
Flaid
Wakks
The Portiand Trail Balzers (33-30) foood
kame
off againat the Toronto Reptore (41-40) in thiilinng game that ended the The game raptors
City
Standard
Raptore winning
close throughout,
13
leading just one point the end of firat quarter 31 50 and extending [-J]
Nasnts
Generation with symbolic references
field-value tuples as regular text (e-g., as a
Trrat stasors
JSON dictionary as in Fig. (1)) and pro-
vide it to an LLM alongside an instruction (e.g., "Summarize a basketball match given its statistics in JSON.").
Visito Taan Stant
City
Partiara
SymGem
faced off against the Toronto Reptons
The Portland Trai Blazers (55
30
41- 19 in thilllnng game that ended
3M
with the Roptors winning 117 116 The
game was a close contest throughout,
Aosente
with the Reptors leacing just one
Symbolically grounded generation. In this paper, we propose a simple sym- bolically grounded generation (SymGen) ap- proach for such tasks. The idea is to first generate a response in symbolic form g E (VUF)* which interleaves regular text and references to fields from the data. For example, in Fig. 2 the response in (2) would be:
Potnts
Trait (teom (xtecter) Peties quarter
and extencing [--]
Compare a standard LLM- generated (A) with a SymGen (B, ours) description of a basketball game, based on match statistics. SymGen imbues spans of generated text (highlighted in blue) with symbolic references to the source data, enabling easier verification: e.g., when hov- ering over a span, the number "30" displays tooltip and link (highlighted in yellow) indicating the value is referencing.
Figure 1:
y = The, visitor. visitor.cityy..../ city....,)..
We can subsequently render ÿ by substitut- ing each field fi E F with its correspond- ing value jj V in the data to obtain the final response. For example, in Fig. 2 the symbolic form (2) is rendered into [The, Portland,... )-J, which is shown in (3). Importantly, the inclusion of symbolic refer- ences enables fine-grained attribution of portions of the text to their generating fields in the data. In this sense, SymGen is similar in spirit to citation-enabled LLMs (Liu et al. 2023 Yue et al. 2023 Gao et al. 2023), except that our approach focuses on providing very precise and easy-to-validate citations. We stress that our aim with SymGen is not to im- prove the overall quality of the rendered response, but instead to ensure its textual quality is maintained while improving the ability of end-users to verify its correctness ($4)
Operationalization. While there are many ways of operationalizing the approach above, in practice we have found that prompted LLMs are highly effective at this task. In general, our strategy consists of prompting the LLM to generate its responses in hybrid form, where fields in the JSON data (which is itself included at the beginning of the prompt) should be referenced using a Jinja-like syntax, ie., the LLM should generate {( field }}
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Published as a conference paper at COLM 2024
STRUCTURED DATA GRON!
SYMBOLIC GENERATION
Faneer
nENDERED
xtatser CMNS
Toa Bloors
Ion
Reptore
(hantana)
8B
o
E fer 1a Ficedulf agaree (E hewr mit E feer war
sgaimst the Terenta Reptoon 43 1o
Portand
410g
Toronts
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thriling para that enced with the Raptors sinning 11? 1E gare
the Reptore winning
etaiter The game waza
30
12
throughout
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leacing
the and ofthe first
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estending thaiiiad sightty by hattinee
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10 and extending their lead siiy hoftiiee
Eir Statttoer use grrvetttr atr cara
Figure 2: Overview of the proposed symbolically grounded generation (SymGen) pipeline, on an example generated by GPT-4. Given the structured data input (1), we use prompted LLM to generate response (2) that contains symbolic references into the data (e.g., the variable visi tor. city marked by A. A parser then substitutes the symbolic references with their corresponding values in the structured data (e.g., Portland for visitor. ci city) to obtain the final rendered text (3). Our SymGen pipeline can implicitly leverage the input data to generate prose without any symbolic references (e-g., the span marked by B It can also transform existing variables (e.g., adding up two numbers, marked by to create unavailable data in the source, while preserving references to the original variables.
to refer to a specific field in the data as opposed to generating the field's value as it would do by default. We can then use an off-the-shelf Jinja parser to render the final text, with minor postprocessing (e.g., based on regular expressions) to make sure we preserve an alignment between rendered references and the field they refer to. Specifically, we consider two methods based of the above:
1. Direct SymGen:
As described above, we encode the structured data as JSON dictionary, and then prompt the LLM to generate its output using Jinja-like syntax.
2. Indirect SymGen:
We also explore a variant of the above, where we prompt an LLM to first generate the response directly without any symbolic references, and afterwards prompt it to convert this response into one with a Jinja-like syntax. Effectively, this consists of first tasking the LLM with building plan, and then conditioning on this plan to obtain the symbolically grounded response.
We detail the used prompts in App. I.2 The advantage of the indirect approach is that it should maintain the same text quality as regular (non-symbolic) generation as long as converting regular prose into symbolicall--aggmente the regular text in the first place; the caveat is that this approach can be more costly with APIs that charge on a per-token basis, as it requires roughly twice the amount of tokens to be generated. We also found this strategy to be unreliable with weaker LLMs such as GPT-3.5 so we only employ this with GPT-4.
text is no harder than generating
This is one of many possible of operationalizations of this method. In particular, we also references (e.g., Python f-strings for the reference format), and alternatives to JSON (e.g., YAML). We found that the LLMs we tested were able to also work with these formats, which suggests that further gains may be possible by exploring other practical operationalizations SymGen.
explored alternatives to rine
Evaluation. As mentioned earlier, the main aim of SymGen is to enhance verifiability of the final text without compromising the textual quality over the final generation. To deter- mine whether this is the case, we study SymGen's performance along four axes. The first is whether the textual quality of the final, rendered text is comparable to the quality of the text generated by a standard, prompted LLM 3 Then we evaluate the citation accuracy,
http/////....xxxeeeiiiieo/o w/exxtessins////////eee/ Using parser also allows us to gracehully handle syntax errors made by the L.M,, e.g. in the event that a Jinja expression that cannot be rendered, one can resolve
For example, httpss//jinjj: pal www.epppooeessss......mo com/en/3. 1.
a default value, e.g., "undefined."
²Specifically, we found that GPT-3.5 would often behave as in the direct SymGen strategy, despite being prompted and given examples of the desired, indirect SymGen behavíor; see 3.1
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Published as a conference paper at COLM 2024
BLEU
BERT FI
ER
GER
BLEU
ROU.-I
ER
GER
GPT-3.5--4 3.5-KK
2-shot 0-shot
Baseline
31.15
53.43
0.00
0.00
GPT--3.5-1 1--6-3
1-shot 0-shot
Baseline
5.87
19.52
0.00
0.00
Direct
26.64
47.88
0.28
0.00
Direct
5.07
19.14
4.00
0.00
Baseline
30.98
54.66
0.00
0.00
Baseline
12.26
22.66
0.00
0.00
Direct
32.43
53.32
3.30
0.09
Direct
9.10
20.65
0.00
0.00
0-shot
Baseline
30.08
54.27
0.00
0.00
0-shot
Baseline
4.94
19.91
0.00
0.00
GPT-4-8K
Direct
33.31
54.07
0.00
0.00
GPT-4-22
Direct
2.24
17.88
0.00
0.00
Indirect
35.02
55.58
0.00
0.00
Indirect
4.66
19.61
0.00
0.00
2-shot
Baseline 32.43
56.39
0.00
0.00
1-shot
Baseline
9.11
22.16
0.00
0.00
Direct 36.83
57.71
2.11
0.00
Direct
7.02
21.78
0.00
0.00
Indirect
37.56
57.88
3.21
0.00
Indirect
8.29
21.33
2.00
2.00
(a) Results
SynthBio (Yuan et al.
2021
(b) Results on Rotowire
WWseman et al.
2017).
Table 1: For SynthBio, we report BLEU (Papineni et al. 2002) and BERTScore F1 (BERT F1; Zhang et al. 2020) against the reference biographies. For Rotowire, we report the BLEU and ROUGE (ROU.LL; Lin 2004) of the generated answers against the reference generations in the dataset. We also report the (general) error rate (ER) and the global error rate (GER).
ie., whether the symbolic references introduced by SymGen are not only correct but also non-trivial in the sense that they cannot be added post-hoc by naïve baseline (54.1), and we further assess whether these links actually enable better verifiability in terms of speed and accuracy ($4.2) via human evaluation. Finally, explore some extensions of SymGen to question-answering and mathematical reasoning ($5). Collectively across all studies, our analysis is conducted on five datasets, which are shown in Tab. 8 of App. Unless otherwise stated, in all cases we compare against a baseline that consists of prompting the LLM generate the response directly, without any symbolic references.
3 Textual quality study
The first empirical study in this work aims to understand whether SymGen generations exhibit the same quality as a (prompted) LLM baseline; our main goal not to improve upon this baseline (although in some cases we observe improvements) as our overarching goal is to improve generation verifiability without sacrificing performance. For this set of experiments, we evaluate on two traditional data-to-text datasets ($3.1), and on new counterfactual obituary generation ($3.2) dataset. For the former, our focus is in analyzing the fluency of the resulting text, whereas in the latter we focus the factuality of the generated text. We consider both zero- and few-shot settings for each of those tasks.
Depending the nature of the task (e.g., complexity, size of the structured data, etc.), we evaluate our approach on mix of GPT-3.5 (with 4K or 16K context windows) and GPT-4 (with 8K or 32K context windows). The full experimental setups, including the prompts, are given in App.D] π Anecdotally, we found that extensive experiments with dif- ferent prompts were not necessary to get L.MSs to perform symbolic generation; we expect that further improvements are possible with more prompt engineering.
3.1 Data-to-text
SymGen is similar in spirit to data-to-text methods, where the goal is to generate some text based on structured data. Classical approaches to this problem consist of designing template based on the schema of the data, and populating it with the values of a datapoint at runtime Kukiich| 1983 McKeown 1992; McRoy et al. 2000). However, relying solely on the schema of the data tends to vield tormulaic text. Neural approaches to data-to- text generation (Wiseman et al.) 2017 2018 Wang 2019 Yin & Wan 2022) improve on this by generating text that is dalapoint-dependen,, but achieve this by training data-to-text
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EM (%)
ROU.-1
ROU.-2
ROU.-L
UR
ER
GER
Baseline
69.05
75.25
53.76
75.23
14.10
0.00
0.00
O-shot
Direct
71.83
76.51
53.89
76.50
14.60
0.00
0.00
Baseline
79.34
83,97
59.87
83,97
5.53
0.00
0.00
2-shot
Direct 78.61
82.90
58.15
82.88
6.76
1.39
0.00
Table 2: GPT-3.5-4K results the automated question-answering evaluation the obituary dataset, which includes counterfactually generated entities. We report the exact match accuracy (EM (8)) and ROUGE (ROU,-1,RO)) ROU.2, ROU.-L; 2004 of the inferred answers from the generated text against the true answer given by the source JSON. We also report the percentage of answers that the QA model did not find an answer for (the unknown rate; UR), and the regular (ER) and global (GER) error rates. Refer to $3.2 for a discussion.
datasets. To this end, we revisit this setting in the context of zero- and few-shot data-to-text generation with LLMs.
Datasets. We first consider SynthBio (Yuan et al. 2021 a collection of synthetically con- structed fictional entities, described in terms of a collection of key-value pairs, which has been used in prior work on templated generation with finetuned models (Zhang et al.) 2022). The task is to generate textual description of the entity. We also consider the Ro- towire WWiseaan et al. 2017) dataset, where the objective is to generate a summary of basketball game given its box (individual player statistics) and line (aggregate team statis- tics) scores. The Rotowire dataset presents a challenging testbed for our approach, since its data structure is substantially more complex than the previous task (i.e., there are many more fields in each datapoint) and it relies on more specific in-domain knowledge (i.e., un- derstanding how to read basketball box scores charts). To keep LLM API costs manageable, we sample 100 examples from the test set to evaluate on. Refer to App. for more details.
Experimental details. On SynthBio, we generate short biography for each entity in the test set using GPTK-3.5-4K and GPTT-48K, and we evaluate against reference texts using BLEU (Papineni et al. 2002) and BERTScore F1 (Zhang et al. 2020). We consider both zero- and 2-shot learning. See App. [2111 for the prompts and example generations. On Rotowire, we explore both GPT-3.5-16K and GPT-432K (the longer context windows are needed due to the length of the JSON), and evaluate against reference texts using using BLEU and ROUGE (Lin 2004). See App. I.2.2 for the Rotowire prompts and examples. We experimented with direct and in-direct strategies on both GPT-4-8K and GPT-3.5-4K models. We found that for many examples GPT--3.5-KK would ignore the instructions/ inn context examples and directly output a response with symbolic references in the first step. Though the metrics were still reasonable (e.g., for SynthBio, BLEU was 32.67 for 0-shot and 33.10 for 2-shot), we omit these results since they do not reflect the correct strategy.
Results. Our results are shown the respective tables for each dataset (SynthBio, Tab. la Rotowire, Tab. [b]. The results for data-to-text generation are generally positive. For Syn- thBio, in the few-shot case, we find that both symbolic generation strategies we considered yield comparable
superior performance to the baseline, indicating that SymGen indeed does not degrade the textual quality of the final generation Interestingly, in the zero- shot GPT-3.5-4K 5.4KK case, direct symbolic generation underperforms the baseline, though this gap is bridged via in-context learning. This could suggest that biographical generation in symbolic space is slightly harder than regular (non-symbolic) generation for GPT-3.5-4K
'For comparison, T (Zhang et al. 2022 which finetunes a pretrained LLM on the full training set-attains 40.30 BL.EU and 14 BERTScore, which are comparable to our GPT-4 results. 4For SynthBio, JSON values are not ideally formatted, meaning that LLMs will either output grammatically incorrect text or have to perform post-processing on-the-fly via more complex tem- plates, which seems to increase the likelihood of errors. Refer to App. 1.2.1 for an example of how more complex Jinja templates can mitigate this.
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For Rotowire, we find that the quality of symbolic generation generally trails the baseline, though this difference is smaller when using GPT-4-32K and indirect generation. Interest- ingly, GPT-3.5-16K seems to outperform GPT-4-32K in this task, as evidenced by compar- ing the baseline results under each model, which suggests that better performance may be obtained by modifying the indirect strategy that GPT-3.5-16K is used to generate sum- maries in regular prose and GPT-4-32K is used to rewrite them using symbolic references.
In many cases, we find that poor symbolic generations arise from rendering errors. To this end, we also report the percentage of outputs whose symbolic form has at least one error (a specific reference in a response failed to render locally and was instead rendered as "undefined"; ; ER) and with at least one global error (where the Jinja parser simply failed to run, causing the whole response to fail and be replaced with "The text is not available.", ;; GER). We find that (i) GPT-3.5 tends to commit more errors than GPT-4 and (ii) providing few-shot examples tends to reduce errors, both of which are expected, though we also find that the indirect SymGen strategy leads to more errors than the direct strategy. We believe this to be because adapting regular, non-symbolic text to have symbolic references can sometimes be hard if the JSON is incomplete or the original text is not written in way that is amenable to the insertion of references.
3.2
Counterfactual text generation
There is evidence that LLMs are capable of memorizing their training data (Carlini et al. 2019 2023) and that this leads to their struggling to generate counterfuctual data that goes against their learnt priors (Hernandez et al. 2023). In this section, we explore whether SymGen retains the same performance the baseline when some of the conditioning data is counterfactual in nature.
Dataset. We collect dataset comprised of 167 famous scientists who lived between 1800 CE and 2000 CE (e.g., Albert Einstein, Carl Sagan, Louis Pasteur, etc.) and further generate counterfactual variants of each entity. The counterfactual variant designed to test for the extent to which an LLM may ignore data that contradicts the information an LLM has seen during training, which has been noted to be common failure mode of smaller language models (Hernandez et al. 2023). For more details refer to App.
Experimental setup. We use GPT-3.5-4K to generate obituaries for each of these (possi- bly counterfactual) entities. For the few-shot experiments, we provide two examples (see App. 1.2.3 for our prompts and some example generations). Unlike SynthBio, we have no reference biographies for these entities, so we devise new evaluation procedure that tries to measure the factuality of the summaries. Specifically, we wrote questions for each prop- erty in the schema of the data, and then prompted GPT-3.5-4K to answer them using only the rendered text (see App. B for details). If the answer was not present in the text, we asked the model to answer with "Unknown." (the percentage of questions answered in this way is reported by the unknown rate; UR). We then computed both exact match accu- racy and ROUGE of the provided responses against the response specified in the JSON; we include the latter as it is provides more leeway in the exact phrasing of the response than the exact match metric. Similar to $3.1. we do not report indirect SymGen results since does not function properly using GP1-3.5-4K.
Results. The counterfactual obituary results are shown in Tab. 2 We find that in the zero-shot case, SymGen slightly outperforms our baseline, whereas in the few-shot case we find that slightly underperforms the baseline; regardless, these differences are small, which again indicates that SymGen likely does not cause any meaningful degradation in the textual quality of the final generation. However, when we condition on whether the model gave response versus stating it did not know the answer (Tab. 6. we find that performance is further improved in the zero-shot case and comparable in the fhw-shot
setting. One possible explanation for this is that symbolic generation includes slightly less information than regular generation (especially in the few-shot case, as seen by the higher unknown rate), which in turn leads to more incorrect answers, since more questions were

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Published as a conference paper at COLM 2024
left unanswered. That is, SymGen seems to favor precision at the expense of recall, which may be desirable in some applications
4 Verifiability Study
4.1
Assessing symbolic reference accuracy
We first aim to determine whether the fields attributed to different spans text are correct given their context. This is important since, in principle, symbolic references may be incor- rect even when the final text is accurate; for example if two fields in our data have the same value, but symbolic reference made to the wrong field, then the rendered text would be correct but not the reference.
Metric. For given generation D, we measure the accuracy of the generated symbolic ref- erences. The accuracy P is defined by comparing whether generated field f E F is the same as the intended fj E F given the context: 1 I - = -fll, where is the number of symbolic references in given generation.
Regex baseline. Besides SymGen, we also use a simple regular expression-based baseline to generate symbolic references. In short, for each field fi in the data, search for its corresponding value vi in the text and attribute any of its occurrences to fi.
Evaluation. We sample 20 test examples from the Rotowire (Wiseman al. 2017 dataset, which is challenging testbed for this study due to the lengthy data associated with ev- ery generation. For each example, we compare the direct SymGen generation to the regex baseline, using either GPT-3.5-16R or GPT-4-32K as the base LLM. Three of the authors of this paper independently annotated the symbolic references in each example according to whether it was judged to be contextually correct not, resulting in in 9068 annotations in total, with an inter-annotator agreement of 97. Overall, we find that when the accu- racy of SymGen is 99.77% and 99 99.52% with GPT-3.5-16K and GPT-4-32K as the base LLMs, respectively, whereas the one the baseline is 35.40% and 46.10% This highlights that not only SymGen is extremely precise when generating symbolic references, but also that such accuracy cannot be obtained with simple, post-hoc methods.
4.2 Human evaluation of improved verifiability
Having established that textual quality is mostly unaffected by SymGen ($3) and that the symbolic references are correct and non-trivial ($4.1), we now turn to whether our sym- bolically grounded generations actually aid in verihability. To do so, we conduct human study to ascertain whether the annotations enabled by SymGen actually aid users in veri- fying LLM generations. We once again focus on Rotowire since it is a challenging testbed for verification: summaries contain many numbers referring wide range of fields, and slight errors might be hard to spot.
Annotation data. We picked five SymGen-generater (Indirect, GPT-4-32K) summaries of different games in the Rotowire dataset and manually verified them for correctness. We then generated three version of each document that were inconsistent with the data by picking random symbolic reference and replacing it with another symbolic reference with a different value that was within 2 units the original value of the field. We prevent the resulting value from being nonsensical, e.g., restrict percentages to be within 0 and 100. For each of these four documents, we created two versions: a version with annotations, and one without any annotations (i.e., if it were generated by regular LLM).
Here we characterize the "precision" in loose sense and leave the formal evaluation of preci- sion / recall for future work.
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0-shot
3-shot
GSM8K
GSM-hard
GPT-3.5-16K
Baseline
93.75
90.63
GPT-4
GPT-3.5
GPT-4
GPT-3.5
Direct
65.63
68.75
CoT
95.0
81.0
64.0
53.5
Baseline
93.75
87.50
PAL
95.0
82.0
79.5
73.0
GPT-4-32K
Direct
87.50
87.50
Indirect
90.63
93.75
SymGen
95.0
79.0
75.0
60.5
(a) Results on the financial QA setting-
(b) Results on GSM8K.
Table 3: For financial QA, we report the acceptability of the answers in the zero- and 3-shot settings. Refer to App. C for details. For GSM8K, we compare SymGen reasoning with other reasoning methods GSM8K and GSM-hard; all approaches were evaluated using GPT-4-8K. Refer to $5.2 for a discussion.
Survey overview. We ask annotators to annotate four summaries: two of them contain SymGen annotations, allowing them to inspect the provenance of different numbers in the text when hovering over number using their cursor, and two containing no annotations (i.e., the output of regular LLM). Each summary is equally likely to come from pool of correct or incorrect summaries, and the first step of each annotation is for the participant to determine whether the summary is correct (i.e., there are no inconsistencies between the summary and the source table) or incorrect (i.e., there an inconsistency between the summary and the table). Crucially, this first step is timed. Answering this question reveals two more questions, asking the annotators to rate their confidence in their answer, and how easy it was for them to reach decision, on Likert scale. We ask these questions in order to measure whether SymGen annotations meaningfully impact (i) human accuracy at finding errors and (ii) how easy it is to verify summary, where both objective ease (i.e., how long did it take to reach a decision) and subjective ease (i.e., how hard did it feel to go through and verify the text). At the end of the study, annotators are provided an exit survey were they are asked to rate whether they prefer annotations (over no annotations) their decisions and (ii) their ease in reaching
with respect to both (i) the confidence decision. We also reward annotators for each question answered correctly to ensure that they are spending adequate effort on the task. More details on the study design, exact questions, and interface are given in App.
Enrollment. We recruited 60 annotators for the study via Prolific. See App. details, including selection criteria, quality filters, etc.
F for more
Results. We find that annotators were equally likely to identify errors regardless of whether SymGen annotations were shown. However, when annotations were displayed, they took average 20% less time to reach decision about whether an error was present or not. This makes sense, since the annotations do not change whether the text is correct or not, they should only facilitate the process of verifying it. We further find that annotators feel 5% more confident of their final answers when using our approach and perceive the verification task to be 14% easier. Finally, from exit survey, we have that 71.67% of par- ticipants agree that annotations made them more confident in their answers, and 83.33% agree that annotations made the verification task easier. In all, the results suggest that SymGen annotations enable a faster and more pleasant verification experience, with some annotators reporting "The annotations help tremendously", "[t]he annotations were nice and definitely helped", and "[...] could still compare the data without annotations it just took longer to find each piece of data". For more details, refer to Tab. 7 in App.
Extensions to other use-cases
In the preceding sections, we established that the textual quality of SymGen is comparable to regular prompted LLM (33), that its annotations are accurate and non-trivial ($4.1) and that SymGen indeed aids in verifiability (64.2). Now, we turn to some extensions of SymGen to question-anaan -nnwering (QA) and mathematical reasoning use-cases.
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5.1 Question answering over structured data
Another possible application of symbolically grounded generation is to enhance verifia- bility in QA over structured data. To evaluate this setting, we construct a dataset of 32 finance-related questions about particular companies (e.g,, "How does the book value of NFLX compare to that of ASML?"), coupled with structured company information. The results in Tab. 3a suggest that with GPT-4, all models offer reasonably comparable text quality across generation strategies, though SymGen underperforms when using GPT-3.5. Refer to App. C for more details.
5.2 Mathematical reasoning
Besides providing symbolic references to fields in the source data, SymGen can also be used to express symbolic computations over the variables, e.g., ces.cpaaauutii computing the half- time points of a basketball game based on the first two quarter scores (Fig. 2 By chaining series of such operations, it opens up the possibility of interleav- ing arithmetic operations within language- based chain-of-thought reasoning. We ex- plore whether this new capability comes without an overall textual quality penalty.
PROBLEM DESCRIPTION
At the trip to the countyy level scavenger hunt competition 90 people required split groups competition begin. bresk people up into smaller groupe with different leaders, 9-person groupe
groups bring back seashells each. how many seashells did bring?
2 SYMBOLIC GENERATION
There people Lases total
and they were split
9 person groups (S group, 65) So there were Os total. ar supo socal, popils alaa groups, it the groups each had members bring back seasinelis each, than (1s ssndwell
aroupard/5 brought back group brought
person
25)), so tothey brought
total sesohella soaatell, grogreseseness peraniggres wup.
Experimental setup. Fig. Gillustrates one approach for performing reasoning via symbolic generation for math problem in GSMSK (Cobbe al.] (021). Each gener- ated computation step is coupled with an assignment statement in Jinja, which re-
seashells.
There were and they were split into prersson gr groups. So there group brought seashalls par person. so they brought back 108 conchells
of groups each had members bying groups brought
lates the natural language explanation of the computation with symbolic expres- sion. Compared to chain-of-thought rea- soning (CoT; Wei et al.) 2022), explicit use of symbolic computations should lead to more easily verifiable computational re- sults of each step; compared to program- aided language models (PAL; Gao et al. 2022) and program of thoughts prompt- ing (PoT; Chen et al., 2023), which recasts problems into programs and executes to obtain a response, SymGen relies more on natural language as a scaffold, embedding symbolic computation within regular text.
Figure 3: Illustration of SymGen reasoning on GSMSK, on an example generated by GPT-4. Given math question (1), the LLM answers via direct symbolic generation, cre- ating variables rendered in a user-friendly manner (3). The syntax we use (Jinja) allows the creation of variables based on the source text (e.g., set- ting total_people to 90, and perform- ing computation by referencing existing vari- ables (e.g., calculating total_groupp based on total_people and group_ sizee, We can moreover explain how computation step lates to previous ones
needed (2), which can be
Dataset. We compare the direct SymGen strategy against CoT and PAL on two datasets: GSM8K (Cobbe et al. 2021) and GSM-hard (Gao et al., 2002).. GSM8K are grade school math problems like the one illustrated in Fig, that typically require multi- ple steps of reasoning to solve. To create more challenging testbed for LLMs, Gao et al. (2022) construct GSM-hard by replacing, for each problem in GSMSK, randomly selected number with large random number of up to seven digits. We problems in the test set of GSMSK and GSM-hard for evaluation, as recent work find that yields similar results to testing on the full 1.3K test set (Wang et al. 2023b)
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Results. Tab. 3b compares the performance GSM8K and GSM-hard using CoT, PAL, and our approach with GPT-3.5-4K and GPT-4-8K. Notably, it seems symbolic reasoning might not be necessary for GSM8K, since both our method and PAL perform similarly to COT. However, when the computations are more complex as in GSM-hard, symbolic gen- eration outperforms CoT by a clear margin when using both GPT-4-8K and GPT-3.5-4K. Our method slightly underperforms PAL on GPT-4-8K, with the margin being larger using GPT-3.5-4K. This is somewhat expected, given that PAL is explicitly geared towards solv- ing such arithmetic problems and moreover makes use of richer programming elements. Inspecting our method's failures on this dataset, we found that many errors arose from sensible (but incorrect) attempts to reason symbolically about math problem using Jiinja notation. See App. G for an error analysis.
Related work
Data-to-text generation. Classic approaches for the data-to-text problem used hand- crafted templates to generate output sentences that interleave text with placeholder to- kens which refer to relevant data fields (Kukich, 1983 McKeown 1992 McRoy et al., 2200).
Carefully engineered templates can provide strict guarantees against hallucinations, but require much manual work to specify, are domain-specific, and may sound robotic.
Data-driven methods for data-to-text initially focused on inducing templates from data Liang et al. (2009); |Angeli al. (2010); Howald et al. (2013); Konstas & Lapata (2013) which 2Hoaldd could be pipelined with a separate generation module. More recent approaches take ad- Parikh et al. (2020) to learn end-to-end
vantage of large-scale datasets Wiseman et al. Parikh
neural networks Wang (2019); [T& Wan (2018); ; Yin & Wan (2022) (See Sharma et al. (2022) for a recent surve.y Of particular note are methods that use template-like structures within neural generative modules to separate out the "what to say" from the "how to say it" Wise-
man et al. (2018),
; Ye et al. 2020); Li & Rush| (2020); Wang et al. (2021); Puduppully et al.
(2022) ; Zhang et al. (002)). These methods typically still require sizable training set, in
contrast to the zero- and few-shot settings considered in the present work.
Tool-augmented LLMs. Recent work has augmented LLMs with tools/ APIs to enhance their capabilities (Schick et al.. 2023; Paranjape et al., 2023 Liang et al., 2023 Shen et al.)
2023 Khot et al., 2023 Qin et al., 2023 Lu al. 2023). These methods enable LLMs to generate string representations of API calls whose results are fed back in as additional context. Our method can be viewed as using a templating language as a tool. Whereas this line of work is usually concerned with enhancing LLM capabilities via external modules, our work instead focuses on using structured data as "tools" to improve verifiability. Our use of arithmetic computations within generations is also closely related to works that generate code snippels along with text to combine the benefits of symbolic computations
and language-based reasoning [Ga et al. 2022 Chen et al. 2023 Zhang et al.) 2023).
LLM verification. LLM verification is an orthogonal improvement to the reliability of LLMs, where the output of an LLM is automatically verified. Yue et al. (2023) and Gao et al. (2023) take an LLM generated response and a reference document it cites and use separate module to determine whether the output of the LLM is entailed from the reference document. Verify-and-ede
Zhao et al. (203) uses self-consistency checks to discover po- tential mistakes, and corrects those mistakes with knowledge retrieval. Poesia al. (2023) allow an LLM to call a logical backend as a tool, so that it can perform logically sound and certifiable reasoning. To the best of our knowledge, our work is among the first to focus on how LLM generations can be made easier to verify by users in data-to-text settings.
7 Conclusion
In this paper, we propose symbolically grounded generation (SymGen) as a simple ap- proach to text generation that yields more verifiable text, by effectively linking portions of the output to the provided input data. Our evaluation suggests that symbolic generation increases verifiability without degrading quality.
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Acknowledgements
We thank AlphaVantage for providing us key for academic use. This study was sup- ported by funds from Liberty Mutual through the MIT Quest for Intelligence initiative. We thank Lauren Yu, Han Guo, Linlu Qiu, and Abbas Zeitoun for their helpful feedback.
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A Discussion & Limitations
One limitation of SymGen is that it cannot completely ensure the avoidance of hallucina- tions. Although it is capable of generating accurate symbolic references, the surrounding context text may still be incorrect, thus leading to verifiable but incorrect generations. Our current studies primarily focuses on close-sourced models; our method to open-sourced models and even finetune models for templated generation, using the dataset generated by SymGen (to the best of our knowledge, currently there are not available training data for this task).
future work is to extend
B Obituary data evaluation
Properties and questions. Tab, 4 lists the Wikidata properties we include in our entities alongside the questions we corresponding question we provide the LLM in the QA evalu- ation.
Evaluation prompt. Refer to App.
1.3 for the prompt use in QA evaluation of
generated obituaries.
Property ID
Property name
QA question
P19
place of birth
Where was born?
P20
place of death country of citizenship
Where did X die?
P27
What was X's country of citizenship?
P106
occupation
What was X's occupation?
P1412
languages spoken
What languages did know?
P26
spouse
Who was X's spouse?
122
father
Who was X's father?
P25
mother
Who was X's mother?
P39
position held
What position did hold?
P166
award received
What awards did X receive?
P140
religion worldview
What was X's religion?
P69
educated place of burial member of cause of death field of work notable work participant in employer student of student doctoral advisor doctoral student nominated for residence academic degree
Where did study?
P119
Where X buried?
P463
What was X a member of?
P509
What was the cause of X's death?
P101
What was X's field of work?
P800
What was X's notable work?
P1344
What was X a participant in?
P108
Who X's employer?
P1066
X was student of whom?
P802
Who was X's student? Who was X's doctoral advisor?
P184
P185
Who was X's doctoral student?
P1411
What was X nominated for?
P551
Where did live?
P512
What was X's academic degree?
Table 4: Wikidata properties and corresponding QA question for the obituary dataset. We use X as placeholder for the entity's 's name.
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C Financial question answering over structured data
Another possible application of symbolically grounded generation is to enhance verifiabil- ity in question-answering (QA) over structured data. This especially motivated by recent work on tool-augmented LLMs (Schick et al. 2023; Karpas et al., 2022), which are able to condition their generation on the results of API calls, which are often given in a structured format.
Dataset. To evaluate this setting, we construct dataset of 32 finance-related questions about particular companies (e.g, "How does the book value of NFLX compare to that of ASML??"), coupled with company information from the Vantage company overview API endpoint Details about the questions and postprocessing are shown in App. D
Experimental setup. We prompt the LLMs answer finance-related questions given some company information. To evaluate the answers, we had one of the authors anno- tate each answer as acceptable or not; in general, an answer was deemed acceptable if the final answer was correct (see App. D for more details). We considered two LLMs: GPT--355- 16K and GPT-4-32K, due to the length of the JSON data. Our prompts and some example outputs are given in App. 13.1 setting.
we used three-shot prompting for the in-context learning
Results. For the financial dataset, we report the percentage responses judged as accept- able under each approach in Tab. 3ab The results suggest that GPT-3.5-16K is unsuited for symbolic generation on this task, falling substantially short of the baseline. On the other hand, for GPT-4 all models offer reasonably comparable performance across generation strategies. See App. G for a qualitative analysis of some errors.
D Financial question answering human evaluation
We had total of 32 questions in the financial QA evaluation; the questions are shown in Tab. 5 There are two types of question: pairwise-comparison questions (pair) and multiway-comparicnn
questions (multi). For the pairwise comparison questions, we only include information from the two companies in question. For the multiway-comparison questions we show data from seven tickers: ORCL, ASML, TSLA, GOOG, CRM, AVGO, NFLX.
The specific structured input provided is the result the company overview endpoint from the AlphaVantage API We applied some minor postprocessing to the API re- sponse to ensure that all fields that are numerical in nature (e.g., SharesOutstanding ciakeetCppaalizaatiin
are actually numerical (i.e., integers or floats) in the JSON as opposed to strings. Note that all questions are answerable from the data provided data (e.g., there is a field Marke tCapitttpllllaaaa alaiiiiionnnn in the structured data that allows the pair_market-cne questions to be answered).
To evaluate the generations, we had one of the authors in the paper annotate each of the responses according to whether the answer was acceptable or not. We adopted a loose criteria for acceptability:
1. If the question had an objective answer (e.g., the answer to "Which company has largest market capitalization, GOOG os ASML?" is ultimately either GOOG or ASML) then the generation was acceptable as long as the final answer given by the question was acceptable. Note that if the reasoning given for the answer was in- correct but the final answer was correct, the answer was still treated acceptable. This choice deliberate, since in certain cases it can be hard to gauge whether
htttp:://ww. http://www..lllhaaattaeeg o///oooueeaatto/////o/ooaooooeer
An example is shown here: https///wwww..a.aaaae.ooooooooor
IBMBapikey=demo
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Question code
Question
pair_market-cap
Which company has the largest market capitalization, GOOG or ASML2
pair_market-cap
Which company has the largest market capitalization, ASML ORCL?
pair_market-cap
Which company has the largest market capitalization, TSLA or CRM?
pair_pe-ratio
How the P/E ratios of NFLX and ASML compare?
pair_pe-ratio
How do the P/E ratios of GOOG and CRM compare? How do the P/E ratios of NFLX and AVGO compare?
pair_pe-ratio
multi_dividend
Which company has higher dividend yield?
multi_revenue-mul
Which company has higher revenue multiple?
pair_book-value
How does the book value of NFLX compare to that of ASML?
pair_book-value
How does the book value ORCL compare to that of NFLX?
pair_book-value
How does the book value of CRM compare that of TSLA?
multi_groth--ooy
Which company grew the fastest YoY in the most recent quarter?
pair_operrating-margii
How do the operating margins of GOOG and CRM compare?
pair_operatting-aarg.
How the operating margins of NFLX and CRM compare?
pair_opperatiiggmarrgl
How do the operating margins of AVGO and TSLA compare?
multi_retunnaassts
Which company has higher return on assets (TTM)?
pair_eps
How does the EPS (earnings per share) of ASML compare AVGO?
pair_eps
How does the EPS (earnings per share) of TSLA compare ORCL?
pair_eps
How does the EPS (earnings per share) of CRM compare to GOOG?
pair_beta
What are the respective market betas for CRM and GOOG?
pair_beta
What are the respective market betas for GOOG and NFLX?
pair_beta
What are the respective market betas for NFLX and AVGO?
pair_50d-avg
How do the 50-d moving averages between AVGO and GOOG compare?
pair_50d-avg
How the 50-d moving averages between TSLA and ORCL compare?
pair_50d-avg
How do the 50-d moving averages between ORCL and GOOG compare?
pair_forward-pe-ratic
What's the difference in the forward P/E ratio between ASML and NFLX?
pair_forward-pe-ratioo
What's the difference in the forward P/E ratio between AVGO and ASML?
pair_forward-pe-ratioo
What's the difference in the forward P/E ratio between CRM and NFLX?
multi_quart-roowh
Which company has stronger quarterly earnings growth (YoY)? How do ORCL and GOOG's EBITDA multiples compare?
pair_ebitda
pair_ebitda
How do ASML and TSLA's EBITDA multiples compare?
pair_ebitda
How do ASML and GOOG's EBITDA multiples compare?
Table The questions that were considered for the financial QA evaluation.
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C-EM (8)
C-ROU.-1
C-ROU.-2
C-ROU.-L
Baseline
80.35
87.5
62.55
87.50
0-shot
Direct
84.07
89.59
63.09
89.57
Baseline 83.95
88.85
63.33
88.85
2-shot
Direct 84.28
88.88
62.43
88.88
Table 6: GPT-3.5-4K results of the automated question-answerrng evaluation on the obitu- ary dataset, when we condition of the model not having abstained from giving an answer, ie., when the answer given by the model was not "Unknown." " We report the conditioned exact match accuracy (C-EM (%)) and ROUGE (C-ROU.-1, C-ROU.-2, C-ROU.-L; Lin 2004) of the inferred answers from the generated text against the corresponding true answers given by the source data. Refer to 332 for discussion.
the reasoning is correct despite the answer being correct. To reduce subjectivity as much possible, we adopted the loosest possible interpretation of acceptability for questions with objective answers.
2. If the answer did not have an objective answer (e-g,, there are multiple sensible answers to "How do ORCL and GOOG's EBIDTA multiples compare?"), then we deem an answer acceptable if the reasoning in the answer is sensible and correct.
3. If the model failed to generate an answer (e.g,, rendering error, or did not answer the question) then the answer was not acceptable.
E
Conditioned obituary results
In Tab. 6 we include the results on the obituary dataset when we condition on the QA model not having abstained from answering.
Details on the human evaluation of improved verifiability
We include further details on the human study below.
E1
Detailed breakdown of summary steps
The following are the different pages the annotators see:
1. Instruction page: They first see page that gives them an overview of the study and the interface, description of the questions they will answer, the structure of the study (with the approximate times to complete each section, alongside infor- mation about compensation), and section with further notes. We make clear here that they are being timed, and that the first three annotations will be practice examples and the next four are the actual examples they need to annotate. For sim- plicity, we use System A to refer to generations without annotations, and System B to refer to the system with annotations.
2. Practice round 1: Gives an example of a document without annotations and with- out errors. Specifically, we say: "The following example, which could have been generated by system A, contains nothing that directly contradicts the table on the left (we have checked it ourselves by going through the different numbers men- tioned in the text, but you will have to do it yourself for future examples). For this reason, you should say that the text is consistent with the source data below (this is an attention check). For these practice questions, it doesn't 't matter what you write down for confidence and ease, but after the practice rounds are finished, you should start answering those."
3. Practice round 2: Gives an example document without annotation but with an er- Specifically, we say: "The following example, that also could have been
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generated by System A, is identical except that it now contains error. Specif- ically, the text says that number of rebounds by Andrew Bogut made was 7, but the number in the table says it is actually 6 (you have to scroll down the table to see Andrew Boguts's statistics). For this reason, you should state that the text is inconsistent with the source data below (this is attention check). Again, since this is a practice question, it doesn't t matter what you answer for your confidence and ease of finding the answer."
4. Practice round Gives an example of document with annotations, and with an error. Specifically, we say: "The example shown below, which illustrates system B's annotations, is very similar but also contains an error: The text says that the number of rebounds that Stephen Curry made 38 points, but in reality he scored 37 points, If you hover your cursor over the number 38, a tooltip will show up, showing that the 38 actually refers to the Golden State Warrior' ' 3Pt Field Goal Percentage. For this reason, you should state that the text inconsistent with the source data below (this is attention check). Again, since this is practice run, it doesn't matter what you answer for your confidence and ease of finding the answer, but you should start answering them properly from now on."
5. Annotation 1: First example they need annotate.
6. Annotation 2: Second example they need to annotate.
7. Annotation 3: Third example they need to annotate.
8. Annotation 4: Fourth example they need to annotate.
Quality checks. We request a participant return their submission if they get two the practice rounds wrong. In our opinion, our instructions for those rounds are clear as to what they should do (i.e., they give the correct answer), so participants that are reading the instructions should get these correct. If they get only one wrong, we allow them to complete the study and compensate them as other participants, but do not use their results in our analysis.
Sampling of annotations. When the user passes the practice round, we show them four documents to annotate. Our sampling process obeys the following properties:
1. Each of the underlying documents is unique (i.e., we pick 4 out of the 5 documents show the annotator)
2. Exactly 2 of the documents are shown with annotations, and two are shown with- out annotations. These may appear in any order.
3. Each document has 50% chance of being consistent with the text. If a document is sampled to be inconsistent, then we sample one of the three possible incorrect variants to show, uniformly at random.
E2 Questions asked to annotators
For each document, the annotators are asked:
1. Is the text consistent or inconsistent with the source data?
Possible answers: Consistent or Inconsistent
Note:
Answering this question stops a timer that begins running when the page is loaded. This is the annotation time we report.
2. How confident are you in your answer? [1 means least confidence (completely unsure), 5 means most confídence (absolutely certain)]
Possible answers: 1,2, 3,4,5
3. How easy was it reach your decision? [1 is hardest, 5 is easiest]
Possible answers: 1,2,3,4,5
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After annotating all documents, the exit survey asked them "Please rate the degree to which you agree with the following statement."
1. System B (i.e., with annotations) gave me more confidence in my answers that System (i.e,, no annotations).
Possible answers: Strongly disagree, Disagree, Neutral, Agree, Strongly agree
2. System B (i.e., with annotations) made it easier for me to do the task than System A (i.e., no annotations).
Possible answers: Strongly disagree, Disagree, Neutral, Agree, Strongly agree
And finally there we added "Optionally, you can leave some comments about the study below." " alongside textbox for comments.
E3 Interface
The following images illustrate the annotation interface:
1. Fig. 4 depicts the instruction page shown to each annotator.
2. Fig. illustrates the full annotation interface for document, after it has been filled out.
3. Fig- illustrates the the exit survey, after has been filled out.
4. Fig. illustrates how the interface changes when you hover over an annotation with your cursor. Specifically, tooltip appears (describing the provenance for the field) and the respective field gets highlighted on the left. The tooltips and the table are both human readable (the values are manually written based on the schema of the data, which is known).
G Qualitative error analysis
Fig. shows some example errors in SymGen generations. Below we include further qual- itative comments on the errors produced by the systems we analysed.
GSM error anlaysis. We inspected GPT-3.5-4K failures by our model. In one case the mnitial_marbles = 'x' %), in attempt to effectively create
model generates (% variable, as one would do when reasoning about problem algebraically. In another case, purple_paine %) was generated in re-
the statement set red_paint whi te te_paint sponse to "There are equal amounts of red, white, and purple paint in the mural," which incorrect Jinja despite making sense conceptually.
Financial error analysis. During our analysis, we found that GPT-3.5-16K struggled with CoTT-lkke (Wei et al., 2022) behavior in symbolic form, e.g., when it required comparing set of numbers and determining the largest one, it would generate references to the num- bers involved in the comparison (as is standard with CoT), but it would not determine the largest one correctly (e.g., Fig. (C). We also found evidence of the models struggling with plausible but syntactically incorrect Jinja generations. In fact, most errors in the symbolic form of the output were because of incorrectly referencing one particular variable, viz., 50DayMovingAver age (e.g., Fig. RD) This suggests that techniques for constrained decoding with LLMs (Shin et al. 2021 Scholak et al. 2021 PPesia et al. 2022 2023 Wang et al. 2023a)
could be employed to curtail syntax errors.
H Human study detailed results
For the detailed results regarding the human study in $4.2 refer to Tab.
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Study on augmenting texts for verification
start upoutt ugainir:
Thank parsspating anuby "our cur cumently tading Pnstt teok, pon_uernttator Pleae sea the instuctionn beion senffuly (sou sinng compe ristted for the lime pise ress thank, and then click the button at the botte of the page to bagh the study
Questions you will answer
hote once have subriittedi..
Overview and instructions
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how you should go about enncitatngg such coumple.
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that Satt Durry had.
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(a) The top half of the instruction page.
(b) The bottom half of the instruction page.
Figure 4: Instructions page shown to every annotator, before they begin the study.
Acc.
Time
Conf.
Effort
SymGen
80.83 + 3.61
1.98 + 0.12
4.63 + 0.07
4.14 + 0.10
Baseline
80.00 + 3.67
2.50 + 0.16
4.44 + 0.09
3.63 + 0.11
Table 7: Results of the human study, comparing system with SymGen annotations to one without. We report accuracy (Acc.), time taken to determine if an example is correct in minutes (Time), and perceived confidence (Conf.) and effort (Effort) on a Likert scale (i.e., 1-5). For Conf. and Effort, higher scores indicate more confidence and less effort respectively. See $4.7 for details. Error bars correspond to standard error on the mean.
I Experimental details
Unless otherwise specified, we run all experiments using the Azure API and use the model version 0613. We state the specific version of the model we use in the main text. We use the SacreBLEU, ROUGE and BERTScore implementations in Huggingface evaluate."
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Annotating game summary 1/4 (system A)
di-genwaeed
Source data
Generated text
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shis contit rupaliiont lead. Butli
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unable
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Questions
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03 03
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Subelt and contrut eiample
Figure 5: Annotation interface for document, as it appears after answering all questions. The second and third questions only appear after the first one is answered, and the submit button is enabled only when everything has been answered.
Exit survey
Thank you participating
Please degree which you agree
System (le., with annotations) gave me more confidence in my answers that System (i.e., no annotations).
Strongly disagree
Disegree
Agree Strongly agree
System (ie., with annotations) made it easier for me do the task than System (i.e., no annotations).
Strangly disagrea
Disagroe Noutral
Agroa
Strangly agree
Optionally, you comments about stucly
Comments
none
Finish
Figure 6: Exit survey at the end of the human study.
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Annotating game summary 2/4 (system B)
Generated text
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Figure 7: Effect of hovering annotation in document that has annotations. A tooltip shows up containing a short description of the field, and the corresponding entry in the table is highlighted.
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Error
The Warriors started strong, [-.]] finishing the first half with score
[I
oos_ugr22
The strong. [-.]] finishing
with score Cleveland emerged victorious, improving
pts_ppri pta_ptt?
Error
The Cleveland Cavaliers emerged victorious, improving their record to
( home wins
Corrected
determine company higher dividend
compare
DividendVield" field for each compeny.
0.016
Context
dividend yield Corporation (ORCI) ORCL Dtviideedhieeddd 33
The dividend yield for ASML Holding NV ADR (ASML) is (( dota. yield
ASM, dota..SK, Csvidenddeed 3)
(AVGO)
eivideneviiel 3)
0.022
Error
Therefore, the company with the higher dividend is
[E data ORCL. Name
Therefore, the company with the higher diliidend yield
[&
Error
The 50 coo-ayymoiing average for Oracle Corporation (ORCIL) Is
data, ORCL. Www.yooodnggiverggge )2
The 50 day moving average Oracle Corporation (ORCI)
Figure 8: SymGen makes the identification of hallucinations easier. In SymGen makes an error (indicated with dark red background) when the model needs to add up two val- ues; in B the model incorrectly increments the values by one. In the model generates correct chain-of-thought, but fails, likely because the symbolic references make it harder to check which value is actually highest. Finally, in we see a sensible but syntactically in- correct Jinja expression, which causes the whole generation to become unrenderable. Note how even when there are errors, symbolic generation provides glimpse into the models' internal reasoning, making it easier understand the motive for such errors (e.g., gener- ating +1 in
Licensing information. The following resources that were used in our paper are licensed as follows (all are English):
1. SynthBio: Apache 2.0
2. Rotowire: Unable to determine
3. GSM: MIT
1.1 Datasets
Tab. sshows the statistics of the datasets we consider. We provide some more details about each dataset and its processing below.
Rotowire. We postprocess the JSON to convert it into a nested, key-value data struc- ture that resembles the output of an API. We found this to be necessary step, and found SymGen to not work well when most of the data is exposed as an array, likely because of implicit chain-of-thoughttiik behavior when the model specifies a key, e.g., [C data. sharaeesshhaaazaaaiieee .fmm }} should be easier for an LLM to reason about than {{ data. tttt.bbxxscores(1]555 }}.
Counterfactual obituaries.
To obtain the entities, we scraped Wikidata collected all for entities with
entities whose occupation is a subtype of scientist, additionally filtering 75 or more sitelinks to ensure relatively well-known scientists were selected. To construct
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Dataset
Domain
Eval #
Field types
Field #
Ref?
SynthBio
Biographies
364
Text
18.96
Obituary
Obituaries
168
Text, Number
20.98
Rotowire
Sports
100
Text, Number
644.16
Financial
Financial
32
Text, Number
127.90
GSM
Mathematics
200
None
N/A
Table 8: Comparison of the four datasets we consider in our study. We note the domain of the dataset (Domain), the number of datapoints we evaluate on (Eval #), the types of the fields of the data, the average number of fields in each example's data (Field #), and whether there is a reference generation for each datapoint in the dataset (Ref?).
the counterfactual variants of each entity, we take each entity's attribute and progressively corrupt subset of its properties 1 by sampling different value from the empirical distri- bution of that property's s values from the collected data. For each entity, we create copies that have 50% and 100% of the selected properties corrupted.
We then constructed dictionary for each entity containing a value for each one 27 manually selected Wikidata properties, e.g,, country of citizenship, place of birth, etc. (see App.B for a complete listing). If an entity did not possess such property in its Wikidata page, we do not add it to the entity's dictionary; if the entity possessed multiple values associated with that property, we selected the first one that was returned by the Wiki- data API. This results in one dictionary of property-value attributes for each scientist. To construct the counterfactual variants of each entity, we take each entity's dictionary and progressively corrupt a subset of its properties T1 by sampling a different value from the empirical distribution of that property's values from the collected data. For each entity, we create copies that have 50% and 100% of the selected properties corrupted. All of these are added back to obtain the final dataset.
1.2 Prompts
We provide the prompts we use in the sections below.
1.2.1 SynthBio prompts
SynthBio prompt (Baseline)
System prompt:
Your task is to write biography about person whose description is given in JSON. You must mention and be faithful about every property of the person in your biography.
Few shot examples (optional):
User: JSON:
json
{
"data": {
"name": "Hanna Skold", "gender": "female". "nationality": "Swedish".
bbirth_date": "14 November 1856", "birth_place": "Gotlandso, Sweden",
10 than name, age and gender. 11 Other than name, age and gender.
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"death_date": "24 December 1935" "death_place". "Santa Cruz, California", "death_cause" "unknown", "start_age": "17",
"notable_ascents" "Pico de Orizaba, Aconcgua, Grand Teton, Cerro Fitzroy", "final_ascent": "Mount Rainier", "partnerships": "Gertrude s. Cooper", "mother": "Karin Andersen",
"children"; "Lillian Skold"
}
}
Assistant:
Hanna Skold (born 14 November 1856) was Swedish mountaineer. She was born Gotlandso, Sweden. She started climbing at the age of 17. She climbed Pico Orizaba, Aconcagua, Grand leton, and Cerro Fitzroy. Skold made her final ascent on Mount Rainier. Skold was the daughter of Karin Andersen and her daughter Lillian Skold. She collaborated with Gertrude S. Cooper. She died December 24, 1935 in Santa Cruz, California.
One more few-shot example like the one above (hidden to save space)
User:
Data for biography generated.
EXAMPLE INPUT
JSON:
1sen
"data"
"nare": "Helaut
'gender": "mal"".
"nationalsty": "Gernan". Stirth_date": "17 August 1863"
"tirth.place": "Nuresberg"
"death_date" date": "Bctober 17, 1947"
"teath_place": "Frankfurt an tain. Sermany"..
artery sclerosis",
"resting_place" "Frankrurt
Germany".
'sina rater "Philospehical University of Briamgen-Nuernbers 1886",
"education"; *Ph. in Philosophy",
"Gecupation": "philosopher. theologtan;",
'notable_ works": "Philosophical
"language": "Gernany".
"genre": "Philosophical Theology",
'avards":
Lutheran Liturgleal Institute University City Frankfurt an 1811",
Chicago; University
"rother": "Berta Kaiser".
"father": *Adolf Nabar
"partner"; Schoanlein".
"children": "Rudolf, Kurt, Gertraud, Marianne, Rosemarie"
Assistant:
LLM generates Synthbio summary without symbolic references.
EXAMPLE E OUTPUT (CPT-4-8K)
Helout Kaiser was renowned German philosopher and theologian, born on August 17, 1863, in Nuremberg, Germany: He completed his Ph.D. in Phalesophy from the Philosophical Universaty of Erlangen
Honorary Erlange-NNembeee Nuemberg 1886. His most notable work Latheran Litungical
Thilosopiical Problems Theism"
University of Chicago and recerved University Grant of the City Frankfurt am Main 1911. He was married to Frieda Schoenlein and they had five children: Rudoli, Kurt, Gertraud, Marianne, and Rosemarie Kaiser passed away from heart disease sclenosis October 1917, Frankfurt am Main, Germany, where he also laid rest
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SynthBio prompt (Direct)
System prompt:
Your task is to write biography about person whose description is given in JSON. You must mention and be faithful about every property of the person in your biography. Instead of responding directly with the text, you must generate Jinja-style expressions in the text with references to fields in the provided JSON, e.g, given JSON that says:
jjson
"data" "place_of ofirrthh_ "Ohio"
then instead of generating "... was born Ohio" you should generate "... was born { data. data.place place_ of f_birthh }}". Note that you can only reference fields that exist in the JSON. The user will be displayed version of this message that has the references filled in, and you should ALWAYS try to such references instead of copying the text directly when possible.
Few shot examples (optional):
User:
JSON:
json
{
"data": {
"name": "Hanna Skold",
"gender": "female".
"nationality": "Swedish",
"birth_date": "14 November 1856", "birth_place": "Gotlandso, Sweden", "death_date": "24 December 1935".
"death_place": "Santa Cruz, California", "death_cause": "unknown". "start_age": "17",
"notable_ascents" "Pico de Orizaba, Aconcgua, Grand Teton, Cerro Fitzroy", "final_ascent": "Mount Rainier", "partnerships": "Gertrude s. Cooper", "mother": "Karin Andersen".
"children": "Lillian Skold"
}
Assistant:
(( data. name }) (born (( data.birth_date }}) was a (( data. nationality )}
mountaineer. She was born in {( data. birth_pacc }}. She started climbing at the
age of (( data. start_age }). She climbed
{( data. setplll_sscentssspllel
')[0] }>,
data. caaabbeeaaccentssspss...
'D[1]
[[ data. notable
'][2] and
[[ data. data.notable nottble_ascents..plle
')[3] }}. [( data. name }) made her final
ascent on (( data. final_aennsseent }). {( data.name
name }} was the daughter of
{( data.mother }} and her daughter was (( data. childen }). She collaborated
with {( data.partnership )). She died on {( data. death_date }} in
{( data. death_place }}.
27

---

Published as a conference paper at COLM 2024
One more few-shot example like the one above (hidden to save space)
User:
Data for biography to be generated
EXAMPLE INPUT
JSON:
data":
"nae"": "Hieluu Kaiser",
'gender": "male"
"nationality": "Gernan", "tirth_date": "17 August 1863". "birth.place": "Nureaberg"
"death_date", "October
1947" Sernary".
"death.place". "frankfurt
"death.cause". "heart disease; artery sclerosis".
'restins.place"" "Frankfurt Kain, Germany".
'sina_rater" "Philespehical University of Erlaneen-Nuernberg, 1886",
'education": "Ph.D. Philosophy".
"eccupation": "philesopherr theologian;",
mnotable_aorks"" "Pailesophicel Problens of Theisn".
"Language". "Gernany",
"genre": "Philcsophical Theology',
'awards": "llondrary Hember of the Lutheran Liturgical Institute at the University of Chicago; university Grant City of Frankfurt an Main in 1811", 'rother": *Berta Kaiser".
'father"; "Adolf
"partner": "Frieda Schoenlein".
"children": "Rudelf, Kurt, Gertraud, Marianne, Rosenarie"
Assistant:
L.M directly generates Synthhio summary with symbolic references.
EXAMPLE OUTPUT (CPT-4)
3) renowned
national iyy
3)
})
{L data. place 3), He was the som data. father 1) and (1 data.ncther }), He studied
{( data alnu_ratee rater splitt" ')(0] 3), where he eamed his (( data, education 3). His most notable work was
"[( data notable srable_.orke 33" written in his native longuage. data. language }). His contributions
data genre })
recognition, incloding becoming an Honurary Member
Liturgical Institute
University
Chicago and receiving University Grant City Frankfurt am Main 1911. He was partnered with (( data, partner })
and they had five children: ( data children )). cata nare 1) passed away on (C data. death. date )) in data. etta.deth..ple due to data. deash His final resting place data.resting.
SynthBio prompt (Indirect)
System prompt:
Your task is to write biography about person whose description is given in JSON. You must mention and be faithful about every property of the person in your biography. After you generate the biography, you will be asked rewrite using Jinja-style expressions.
Few shot examples (optional):
User:
JSON:
json
{
"data": {
"name": : "Hanna Skold".
"gender": "female"
"nationality": "Swedish".
"birth_date": "14 November 1856"
"birth_place"; "Gotlandso, Sweden",
28

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Published as a conference paper at COLM 2024
"death_date": "24 December 1935" "death_place". "Santa Cruz, California", "death_cause" "unknown", "start_age": "17",
"notable_ascents" "Pico de Orizaba, Aconcgua, Grand Teton, Cerro Fitzroy", "final_ascent": "Mount Rainier", "partnerships": "Gertrude s. Cooper", "mother": "Karin Andersen",
"children"; "Lillian Skold"
}
}
Assistant:
Hanna Skold (born 14 November 1856) was Swedish mountaineer. She was born Gotlandso, Sweden. She started climbing at the age of 17. She climbed Pico Orizaba, Aconcagua, Grand leton, and Cerro Fitzroy. Skold made her final ascent on Mount Rainier. Skold was the daughter of Karin Andersen and her daughter Lillian Skold. She collaborated with Gertrude S. Cooper. She died December 24, 1935 in Santa Cruz, California.
User:
Your job is to now rewrite the answer you provided above, but instead of responding directly with the text, you must generate Jinja-style expressions in the text with references to fields in the provided JSON, egg., given JSON that says:
json
{
"data": {
"place_of_birth" "Ohio"
}
}
then instead of generating "... was born in Ohio" you should generate "... was born in data.place place_of...... _f_rrhh }3" Note that you can only reference fields that exist in the JSON. The user will be displayed a version of message that has the references filled in, and you should ALWAYS try to use such references instead of copying the text directly when possible. Remember, you MUST make sure you ONLY use fields that exist in the JSON. You must NOT make up fields.
Assistant:
(( data. name }) (born (( data, birth_hirt }}) was a (( data. nationality )} mountaineer. She was born in [{ data. birth.pace }}, She started climbing at the
age of {{ data. start_age }). She climbed
data. notabllleeee... pettable_sscens....
')[e] }},
{( data. sttaabeessscettssppl...
')[1] }},
{( data. data.notable satplleaaccentssspl.e
')[2] }}, and
[[ data. sitabbe_asceetssspplttt
')[3] }}. {( data. name }) made her final
ascent
data. finaaffiialaaemm }}. (( data. name }} was the daughter of
{( hata.mother }} and her daughter was (( data. children }). She collaborated
with (( pata.partner ships }). She died on {( data.death_date }} in
{( data.death_pp
}}
One more few-shot example like the one above (hidden to save space)
User:
Data for biography generated
29

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Published as a conference paper at COLM 2024
EXAMPLE INPUT
JOON:
Joon
data":
"Helaut Kaiser", 'gender": "maie"..
'nstionality". "Gernan". "birth_date"" "17 August 1863",
"tirth.place": "Nuresberg"
"death_dace". "October 17, 1947".
"ceath.place". "Frankfurt an sain. Sermany"..
"death.couse". "heart disease;
sclerosis",
"resting-place". "Frankfurt
Gernany",
raterr "Philospehical University of Erlangen-Nuerbberg 1886",
'education"; 'Ph.D. in Philosophy",
"eccupstion" "philesopher. theologian;",
"notable_ works"; "Pailosophical
'language": "Gernany"
"genre": "Philosophical Theology",
"awards":
Hember
Lutheran Liturgical Institute the University of an 1911",
Chicago; University
City
"rother": "Berta Kaiser".
'father': "Adolf Neter
'partner"; "frieda Schoemlein',
"chiidren": "Rudoif, Kurt, Gertraud, Marianne, Rosemarie"
Assistant:
LLM generates SynthBio hingraphy without symbolic references
EXAMPLE OUTPUT (GPT-4-8K)
Helmut Kaiser was German philosopher and theologian, born on August 17, 1863, Nuremberg, to Adolt Weber and Berta
Kaiser, He studied the Philosophical University Brtangen-Nembbrgg graduating 1886 with a Ph. in Philosophy, His
genre Philosuphical Theology, Latheran Litungical Institute the University Chicago and receiving the University Grant of the City Frankfurt am Main in 1911. He was partnered with Frieda Schoenlein and they had five children: Rudolf, Kurt, Gertrand, Marianne, and Rosemarie. Kaiser passed away October 1917, artery sclerusis. He laid
works include Thilosophical Problems Theism" He
received several awards, including becoming Homorary Member
Frankfurt Germany,
city.
User:
Your job is to now rewrite the answer you provided above, but instead of responding directly with the text, you must generate Jinja-style expressions in the text with references to fields in the provided JSON, e.g., given JSON that says:
jsson
"data": {
"place_of_birth", "Ohio"
then instead of generating "... was born in Ohio" you should generate "... was born in {[ data. tttt.placefofbbllt of_birth )}" Note that you can only reference fields that exist in the JSON. The
will be displayed version of this message that has the references filled
in, and you should ALWAYS try use such references instead of copying the text directly when possible. Remember, you MUST make sure you ONLY use fields that exist in the JSON. You must NOT make up fields.
Assistant:
LLM rewrites biography so it contains symbolic references.
30

---

Published as a conference paper at COLM 2024
EXAMPLE OUTPUT (GPT-4-8K)
nane 2) was (1 data. nationality 3) occupatiooon }), bom [( batt }),
[( data birth_place to (( data. facher 33 and (C data mother 3). He studied at the
data almu raterater,.ae split( '[ee] gradduating in data. alturaarrr splin('. 1D013 3) with data, education }).
His notable works include '([ data.nstable, warks 11. wrote
language and data genre }). received seceral awards, including becoming an E data, awards, split(;; ')ee]
receiving
[[ data maads. spliiç";; 2013 }). He was partnesed with (( data, partner 23 and they had live children:
data children }). [[ data. nare passed away (( data death_date 37, in data death_place )), to
33,
in resting-ples }).
1.2.2 Rotowire prompts
Rotowire prompt (Baseline)
System prompt:
Your task write summary of a basketball game whose statistics are given JSON. You must remain faithful the provided statistics your summary.
Few shot examples (optional):
User:
JSON:
json
{
"data": {
"home_line"
"name" : "Magic",
"city":
"Orlando",
"wins" 6,
"losses": 9.
"pts": 92,
"pts_qtr1". 18,
"pts_qtr2": 28,
"pts_qtr3": 21,
"pts_qtr4": 25,
"fg_pct": 45,
"fg3_pct": 19,
"ft_pct": 70,
"ast": 18,
"reb": 44,
"tov": 14
"visitor iisstor._a line"
" "name": "Heat" "city": "Miami", "wins": 7.
"losses": 6,
"pts": 99,
"pts_qtr1": 22,
"pts_qtr2": 27
"pts_qtr3": 19,
"pts_qtr4": 31,
"fg_pct": "fg3_pct": 46,
"ft_pct": 72,
"ast": 17,
"reb": 33,
" "tov": 16
box box_score";
"luol_deng".
31

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Published as a conference paper at COLM 2024
"player_name" "Luol Deng", "first_name "Luol", "
"aaeond_nme"" "Deng", "city": "Miami", "start_position" "F",
"min": 33,
"pts": 9,
"fgm": 4,
" "fga": 9,
"fg_pct": 44,
"fg3m" 1,
"fg3a": 2,
"fg3_pct": 50,
"ftm":
"fta": 0
"ft_pct": 8,
"reb": 7
"oreb" 1,
"dreb"
"ast" 0
"stl" : 1
"blk": 0
"to": 2,
"pf":
Write summary of the game described above.
Assistant:
The Miami Heat (7-6) took down the Orlando Magic (6-9) 99-92 Saturday night at the Amway Center Orlando. The Heat were just too much for the Magic when all was said and done, as they made a nice run in the fourth to get a seven-point win. What makes the win even more impressive is that Miami did it without guard Dwyane Wade (hamstring). The Heat got plenty of production from some key players help get the job done though. They were led in scoring by Chris Bosh, who put up 32 points (13-20 FG, 4-6 3Pt, 2-2 FT) to go with 10 rebounds. Mario Chalmers had huge night also, scoring 24 points (10-16 FG, 2-5 3Pt, 2-4 FT) to go with eight assists and five rebounds. Miami fans have to be happy to rookie Shabazz Napier stepping up big, he played extended minutes (28), and scored 12 points (4-8 FG, 2-5 3P5 FT) to go with two rebounds, two steals and one assist. Miami has quick turnaround, as they take on the Charlotte Homets at home Sunday night. The Magic just couldn't get enough production to outscore the Heat on this night, only three players hit double digits, with Nikola Vucevic having monster night, scoring 33 points (12-24 FG, 9-11 FT), with rebounds. Vucevic was just dominate, and grabbed 11 offensive boards on the night. The other production came from Victor Oladipo, who had 14 points (5-11 FG, 1-4 3Pt, 3-5 FT), with four rebounds, two assist and two steals, well as Evan Fournier, who scored 11 points (5-15 FG, 1-5 3PT) with six assists and rebounds. The Magic get night off now, before taking on the Cleveland Cavaliers on Monday.
User:
Data for game to be described
32

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Published as a conference paper at COLM 2024
EXAMPLE INPUT
JOON:
Joon
data
Tine""ine":
"aaee" "Hawks".
"city"
"Atlanta".
"wins" " 46.
"pts"" 95,
"pts_atri": 28.
"pts_atr2": 14.
"pts_qtr3": 34.
"pts_atri" atre"; 19.
"fg_pet": 11.
"fg3_pet": 45,
"ft_pct::
"ast": 27.
"reb": 42.
"tov":
"yisiter line": [
"nane" "Magic",
"city". "Orlando", "wins":
"lesses": 41.
"pts": 28,
"pts_atri": "pts,
"pts_atr3": 22.
"pts_atr4"; 21.
"fg_pet":
"rg3_ pct": 35.
"ft_pct" 54.
ast 22.
"reb":
_ox_score";
"tobias_ harris"
"alayor_nane""
Harris",
"First_name" "Tobias". "second_nane" "amedd_amee" "Harris", "city": "Driando" "scart_position" "p". "ein": 37.
"pts": 16, "fge":
"rga":
"fg_pct": 56.
"1g3n": 2.
"fgaa":
"fg3_pct";
*fte": 3,
"fta": 4.
"re_pct";
"oreb": 6,
"dreb":
"ast":
"bik": 1
"to": 2.
"pr":
Write summary the described ahove.
Assistant:
LLM generates game summary without symbolic references
33

---

Published as a conference paper at COLM 2024
EXAMPLE OUTPUT (GPT-4-32K)
score strong with 28-point tirst quarter and maintained their lead throughout the game, despite elight dip in the second quarter. They managed shoot 44% from the field and 45% from the thrce-pointt line. The Havkss also outrehounded the Magic 42-40 and had
(46-12) secured victory against Orlando Magic (19-41)
Howks
The Hawks' top performers were Paul Millsap with 20 points, Horford with 17 points and 13 rebounds, and DeMarre Carroll with 15 points jeff Teague also contributed significantll with points and assiots.
On the other side, the Magic struggled with their free throws, making only 54% of them. However, they managed to shoot 45% 35% bevend grabbed rebounds. Despite their elforts, the Magic couldn't overcome the Hawks' strong performance, resulting, their 41st loss of season. The Hawks, the other hand, continued their impressive run, securing their 46th win.
solid performance with points, while Tobias Harris points points.
Magic's standout player Nikola
Oladipo
Rotowire prompt (Direct)
System prompt:
Your task is to write summary of a basketball game whose statistics are given in JSON.
You must remain faithful to the provided statistics in your summary. Instead of responding directly with the text, you must generate Jinja-style expressions in the text with references fields in the provided JSON, e-g,, given JSON that says:
jssjson
"data":
"flace_of_birth;; "Ohio"
then instead of generating "... was born in Ohio" you should generate "... was born in {{ data. place_of__ of_birth }}". Note that you can only reference fields that exist in the JSON. The user will be displayed version of this message that has the references filled in, and you should ALWAYS try to use such references instead of copying the text directly when possible.
Few shot examples (optional):
User: JSON:
json
" "data": {
chom__inng
"name": "Magic",
"city": "Orlando".
"wins": 6,
"losses"; 9.
"pts": 92.
"pts_qtr1": 18.
"pts_qtr2": 28,
"pts_qtr3": 21,
"pts_qtr4": 25,
"fg_pct": 45,
"fg3_pct": 19,
"ft_pct": 70,
"ast": 18,
"reb". 44,
"tov": 14
visitorr _inrelnin_ : (
"name": : "Heat"
"city": "Miami",
"wins": 7.
34

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Published as a conference paper at COLM 2024
"losses": 6,
"pts": 99,
"pts_qtr1", 22,
"pts_qtr2": 27,
"pts_qtr3": 19,
"pts_qtr4"; 31,
"fg_pct": 52,
"fg3_pct": 46,
"ft_pct": 72,
"ast": 17,
"reb": 33,
"tov": 16
box box_scoore:
" "luol_ "ouol_deng::
paaayer_ame"" "Luol Deng",
"first_name" "Luol", "second_name" "Deng"
"city" "Miami". "start_positions "F",
"min": 33,
"pts": 9.
"fgm": 4,
"fga": 9,
"gg_ptt; 44
"fg3m" 1,
"fg3a": 2,
"fg3_pct" : 50,
"ftm": 8,
"fta": 0
"ft_pct": 0,
"reb": 7
"oreb"
"dreb" : 6,
"ast" 0,
"stl" " 1,
"blk"
" "to": 2,
"pf":
Write summary of the game described in the JSON above. Remember to ALWAYS use Jinja-style expressions reference the fields the JSON whenever possible.
Assistant:
The {{ aata.visitor_ll lin...iie.... city }} { data. visitor_ name }}
(E( fata.visitor__ine wins })-(( data. visitor_line losses }}) took down the
(( loome_line.cii city }) data. home_line name }}
(C[ data.home_line wins }}-(( data. howe_lineeen....o losses }0)
(( data. witaaviiiitoorlieee.... pts }}-({ data. home_line. pts }} on Saturday night at the
Amway Center in {{ doma.home home_line city }). The Heat were just too much for the Magic when all was said and done, they made nice run in the fourth to get seven-point win. What makes the win even more impressive that Miami did it without guard Dwyane Wade (hamstring). The Heat got plenty of production from some key players to help get the job done though. They were led in scoring by {( data.box_Stt box_score chris_ bosh. player_name }}, who put up
{( bott.bbx_score chris bosh. pts points
(C( data. data.box_scom box_score. chris bosh. fgm ))-(( bata.box_scorf www.ree.crrissboss.. fga }) FG,
35

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Published as a conference paper at COLM 2024
{( data.box_scomm box_score. chrischrroo fgåm }}H{ data.box__ www.booxxo..e.oeeeeeeeeeeeeeeeep cotore.chris.bostee fg3a }}
3Pt, { data. iita.box_score wwx_sscorecchh chris_ mooshffmm ftm }H((( bata.box_see www._score.chrss.oo.... chris_bosh. fita
FT) to go with {f data. score, chris..ohhs. reb }} rebounds.
(( data. box_score..eeee www__scoee.maroochheee www.oocchalmerrss wawplleersslllerrreeeeee player_nan _aae }} had a huge night also, scoring
{( data. box_score. www.score..arroooccalaleee.
}) points (CC data. data.box_score. box_score.
vario_chalmeed fgm ))-(( fitt.box_sccrf mario_chiii ctloooooaeerss..ee fga FG,
{( data. bow_score.....eeeooooooooooeee.m wwt..mmaaaaaaleeeeeeeee fg3m ))-(( data. bittaboo__ccree www.sr.errrrooooooo mario_chalmerss.
fg3a }} 3Pt,
{( data. box_score. maari_chaalmaeeee ftm ))-((
marim_ chalmers. fta }} FI) to go with {( bata.box_seee maaii_chaalmeeeeeeeeeaaeeaaeeeee ast }} assists and { data. www_soooeee.e.ooceeoooooeoooeee.
crtrrio_chalmes..eee }} rebounds. Miami fans have be happy see rookie {( data. box nox_score... shpbarraaaiierrrrrrraearrrr
}} stepping up big,
as he played extended minutes ([( dat..bor..corr..saaaz..... .min }}), and data.box_scoe
scored {( data. box_score wwt.:ssaaaaaaiiieeeeee pts }} points ((( data. box_score.
shabazz_napier... fgm })-(( fata.box__at
shabazz_napier fga FG, (( data. bot_aooxx_ccre.... shabazz_napier fg3m } })-(( data. box_score. shabazz_napier.
fg3a }} 3Pt,
{( data.box._t box_score. shabbzz.aaazzz. ftm })-(( data. tltta.bo__seeee box_score shabazz_napier.
fta }} FT) to go with {{ data.box_score www.azhaaaaaaaaaaaa napier. reb }} rebounds,
data. box_score wtt.sscre.saaazz__apiiieeee shabazz_napier.stl }) steals and
}} assist. Miami has a quick turnaround, as they take on the Charlotte Hornets at home Sunday night. The Magic just couldn't
{( data. box_score.....eeeoooooooo.o.ee._ www.scrreesaazz_eaaeeee shabazz_napier
get enough production to outscore the Heat on this night, as only three players hit
double digits, with (( data. data.box_scon www_scoreeeee.oeeeeoooooeo...eeb nikola_wwcevicc vucevic player_name }} having monster night, scoring {{ data. _ox_sccoe..o www.olalavvvvviicccc.a.. {( data. www.bboo_ooooooeeeeeee works_screeenee www.l..ikolaaa...o.....oo....aaa wtttt.oo_sccrreniklla.eeeeeee
wwwoollccccviicppt.ccc. }} points (C( data.
})-(( data. box_ score. www.ll.wwceeiicc...omo.
}} FG, ftm })-(( data. score. wwwooreniikllaaeeeo vucevic.
fta FT), with {{ data. battttabo_sccoeeuee wiw.rreeeniollaaveeiiiei
}} rebounds. Vucevic was
just dominate, and grabbed [[ data.box_score www.ola..ucevvi..oree
offensive
boards on the night. The other production came from
(( data. box_score. diltorrooaddne
player_ }), who had
{( data.
victor cowioooooooooooo.oooo .ts }} points (C( data. box_score.
data.box_score
dictor_oladip fgm })-({ data. bot.aboxxcccre.... victor_oladia fga FG,
{( data. bow_score.e..ooo cicctr_oolddipo. fg3m })-({ data. box_score.
data.box_scorf victor_oladipo
fg3a }} 3Pt,
{( data. www.bbooo..viccteee.oooooo.ceeee
ftm })-(( cttta.box_score..o www.aboco.eeeeicoro.ooloooooo victor_oladipee
fta }} FT), with [[
victor_ole peatorr_laadio... .adiioorebbb reb }} rebounds,
{( data.
data.box_score box_score
www.oooe.iitroollee.ooooo.o victor_oladipo........ ast }} assist and
(( data. box_score. wwccoor.iieto............oooo..e victor_oladipo.s stl }} steals, as well as
(( data. box_score.eeeeoooooooooooooo..eo wictorreevaanneeeee evan_ fwan__forrnierrpaaaee
}), who scored evan_fournier.pts }} points (C( bata.bbx_sccoe
{[ data.
evan_fournie fwwrffor..reeeee fgm })-((
evan_fournier
}} FG,
data. box_score._eoooooo Lwva_fournier.ff fg3m })-(( data. casta.boo__cooo nox_score.eevn_foee fournier.
fg3a }} 3PT) with (( data. www.ssorre.eva_fooe...
.ast ast }} assists and
(( data. box_score wwweeeeeeaaaaeee aaran_ffuniirrre reb }} rebounds. The Magic get night off now,
before taking the Cleveland Cavaliers on Monday.
User:
Data for game to be described
36

---

Published as a conference paper at COLM 2024
EXAMPLE INPUT
JOON:
Joon
data
Line""ine":
"aaee" "Hawks".
"city"
"Atlanta".
"wins" " 46.
"pts"" 95,
"pts_atri": 28.
"pts_atr2": 14.
"pts_qtr3": 34.
"pts_atri atre"; 19.
"fg_pet": 11.
"fg3_pet": 45,
"ft_pct::
"ast": 27.
"reb": 42.
"tov":
"yisiter line": [
"nane" "Magic",
"city". "Orlando", "wins":
"lesses": 41.
"pts": 28,
"pts_atri":
att=_t
"pts_atr3": 22.
"pts_atr4"; 21.
"fg_pet":
"rg3_ pct": 35.
"ft_pct" 54.
ast 22.
"reb":
_ox_score";
"tobias_ harris"
"alayor_nane""
Harris",
"First_name" "Tobias". "second_nane" "amed_ "Harris", "city": "Driando" "scart_position" "p". "ein": 37.
"pts": 16, "fge":
"rga":
"fg_pct": 56.
"1g3n": 2.
"fgaa":
"fg3_pct";
*fte": 3,
"fta": 4.
"re_pct";
"oreb": 6,
"dreb":
"ast":
"bik": 1
"to": 2.
"pr":
Write summary of the game described in the JSON above. Remember Al WAWS use Jinja-stylee expressions to reference the fields the JSON whenever possible.
Assistant:
L.M generates game summary using symbolic references.
37

---

Published as a conference paper at COLM 2024
EXAMPLE OUTPUT (GPT-4-32K)
nore, son__1ine.c1. 13 data, hose_ Line nane 33,(( Linea Lineo_iine.wdreg }3-(( data. hove_ losses }3)
deforted the (t data visitor_ Line, city 3) data.visitor... Saire.rirr...... nome 33
(CC data, visitor_ Tine wins })-(C data, visitor_ Sorr__nee..ost Josses )3)
13-(1
_ine.pps The Hawks had shrong third quartee, scoring.
here. hore.line, pts_qtra 1) points, which helped them secure the victory, They shot (( data hove. Fg-pet 37%
from the field, [( data, howe__inn Tine. Fgt_pct 13% from beyond the art and (( data, hore, line ft_pct 17% from the free-throw
line. They also had data. stre.oor___lee
assists, hore. Mine__inerrr reb rebounds, and (( data. hiw. tov })
pta_qtri points They shot (t data visitor_iiinee pero__ino...gpee 35% from field, [1 data. visitor Hin.. rga_pct 17% from three point range, and diappointing (( data visitor_line Ftypet 37% from the tree-thow line. They had ( data, visi _ine, ast assists,
Magic,
hand, struggled
quarter, scoring only (( data, visitor
For Hawks, ( data box_score, millpp player_nane 1)
team with data box score, piulmillsppp pts })
points, (C data, box score paul_aillsop reb rebounds, and ( data, box score paul_aii Isap ast assists.
data acore al_horford, pordd.plann player name 3) had strong game with (( data, mox_score.al horrooooroooooooooooooooo }) points
resuunds.
Foe Magic, 30 data bor_acore, vucevii, player_nare }) had standout performance
[E data box score vikola_vccevic pts }) points [( data box score. wew.e. wac.llaaavveeeeeeeeeoe rab )) iehounds.
data tobiashharris player_rane }) alsu contributed with data, box,score, tobias, www.ssharrss.pp.. }) points tabiat_harris.no }) rebounds.
Rotowire prompt (Indirect)
System prompt:
Your task is to write summary of a basketball game whose statistics are given in JSON. You must remain faithful to the provided statistics in your summary. After you generate the summary, you will be asked to rewrite it using Jinja-style expressions.
Few shot examples (optional):
User:
JSON:
>*> json
{
"data": {
home 'homeliine"
"name" "Magic",
" "city": "Orlando"
"wins": 6,
"losses": 9,
"pts": 92,
"pts_qtr1": 18,
"pts_qtr2": 28.
"pts_qtr3": 21,
"pts_qtr4": 25,
" "fg_pct": 45,
" "fg3_pct"; 19,
"ft_pet": 70.
"ast": 18.
"reb": 44,
" "tov": 14
)
v sisitor_line""
"name": "Heat"
"city": "Miami",
"wins": 7,
"losses": 6,
"pts": 99,
"pts_qtri": 22,
"pts_qtr2": 27.
"pts_qtr3": 19,
"pts_qtr4" 31,
"fg_pct": 52,
"fg3_pct": 46,
38

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Published as a conference paper at COLM 2024
" "ft_pct:: 72, "ast": 17,
"reb": 33,
"tov": 16
box box_score": (
aluol_deeng
" "player_name" : "Luol Deng",
"first_name": "Luol". "second_name". "Deng", "city": "Miami", "start_position "F", "min": 33,
"pts": 9,
" "fgm": 4,
"fga": 9,
" "fg_pct": 44,
"fg3m" 1,
"fg3a": 2,
"fg3_pct"s 50,
"ftm": 0
"fta": 0
" "ft_pct": 0,
"reb": 7
"oreb": 1,
"dreb" 6,
"ast" 0,
"stl" :
"blk":
"to": 2,
"pf": 3
}
Write summary the game described above.
Assistant:
The Miami Heat (7-6) took down the Orlando Magic (6-9) 99-92 on Saturday night at the Amway Center in Orlando. The Heat were just too much for the Magic when all was said and done, they made nice in the fourth get a seven-point win. What makes the win even more impressive is that Miami did it without guard Dwyane Wade (hamstring). The Heat got plenty of production from some key players to help get the job done though. They were led in scoring by Chris Bosh, who put up 32 points (13-20 20 FG, 4-6 3Pt, 2-2 FT) to go with 10 rebounds. Mario Chalmers had huge night also, scoring 24 points (10-16 FG, 2-5 3Pt,
FT) go with eight assists and five rebounds. Miami fans have to be happy to see rookie Shabazz Napier stepping up big, as he played extended minutes (28), and scored 12 points (4-8 FG, 2-5 3Pt, 2-4 FT) to go with two rebounds, two steals and one assist. Miami has quick turnaround, as they take on the Charlotte Hornets at home Sunday night. The Magic just couldn't get enough production to outscore the Heat on this night, as only three players hit double digits, with Nikola Vucevic having a monster night, scoring 33 points (12-24 FG, 9-11 FT), with 17 rebounds. Vucevic was just dominate, and grabbed 11 offensive boards the night. The other production came from Victor Oladipo, who had points (5-11 FG, 1-4 3Pt, 3-5 FT), with four rebounds, two assist and two steals, as well as Evan Fournier, who scored 11 points (5-15 FG, 1-5 3PT) with six assists and six rebounds. The Magic get night off now, before taking on the Cleveland Cavaliers on Monday.
39

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User:
Your job is to now rewrite the answer you provided above, but instead of responding directly with the text, you must generate Jinja-style expressions in the text with references to fields in the provided JSON, e.g., given JSON that says:
json
{
"data": {
-lace_of_birthh "Ohio"
}
}
then instead of generating ".. was born in Ohio" you should generate "... was born in {( data. data.place_on salac_eo_birrreeeeeelllleeeeleee 33". Note that you can only reference fields that exist in the JSON. The user will be displayed a version of this message that has the references filled in, and you should ALWAYS try to use such references instead of copying the text directly when possible. Remember, you MUST make sure you ONLY use fields that exist in the JSON. You must NOT make up fields.
Assistant:
The {( visivvviitoo_iiiii city }) {( data. visitor cirtior__iieeeee name }}
(CC data. visitor_line wins })-(( data. iisioor_linee losses }}) took down the
{{ data.hone hom__lineeeee..
}} [{ data. home home_line name
}}
(C( data.hhme_line wins })-({ hata.home__ line. losses }})
{[ data. visivorrittroriieeee pts }}-{{ data. home lime_line.pt }} on Saturday night at the Amway Center in {{
city }}. The Heat were just too much for the Magic when all was said and done, as they made nice run in the fourth to get seven-point win. What makes the win even more impressive that Miami did it without guard Dwyane Wade (hamstring). The Heat got plenty of production from some key players to help get the job done though. They were led in scoring by {( data. box_score phris_bosh player_name }3, who put up
{{ data. box_score chris_ bosh.
points
(C(
chris __osh.. f.m ))-(( data.box_scom wwwis_brrsss..eee fga }} FG,
{( data. www.booooeee_oeeeeooooeooooeeeee chris_horssssee fg3m }3-(( data,
www.boooeeeeeoeeeeeeeeeeeeeeeeee cwrorr.crri.... chris_bosh fg3a }}
3Pt, [( data. tattabbx_sccreee www.s.ccrrrii......ee chris_ ftm }}-{(
www.r..crris........... chris_bosh fta hwctrrisssbshhhrreee }} rebounds.
FT) to go with [[ dot.abo.__ateeeeaaaeeeoeeeebeeea
{( data.box_saa box_score waweeeeaaree...eeee wwwrriiooohallrrs..om player_name }} had huge night also, scoring
{( data. bwx_score..e.oo..oo.oooo....eeeb fox__sore.marro_.co mario_chalmers
}} points (C( data. box_score.
mario_ aario_chalmerss fgm ))-((
mar mario_chaalmeee tooochhaleers....o fga )) FG, www.ma.chaaamrsss........ fg3m }--({ data. box_score. www.maacraaalllaaaaaa chalmeeeess.....
{C data. box box_scoree.meeem wwccoreemario..oom
fg3a }} 3Pt,
data. www.boooeee...eeeeooooooooooeee_ mario_chalmee
})-({ data. fota.box_ss0 box_score.._cccccoooooooooooeeet pario__hhalmen
fta }} FT) to go with {( data. www.sccoreemario_ahhaaaea
}) assists and
(( data. foo__score.mer pario__hhalerre reb }) rebounds. Miami fans have to be happy bow_score..haaazzz_nell/ee
to see rookie (( data.
box_score. box_sbox_
player_name }) stepping up big,
he played extended minutes (ff data. wow_bboo__oooooooeooe score, wwwrse..aaae...nnaeeeee shabazz_napies min })), and scored {C data. box_score. wwworeesaaaaz.nnooeeooee
wwwbazz_napierr..... pts }} points (C( data.box box_sore._c shabazz_napier. fgm })-({
wwwbazz_napieereeee fga }} FG,
{( data. www.booww..o.oo..o.oo.....eeee.p www.b/ssoorresaaaaeaaaiiieeeeeee
fg3m }-[[( data. wwo._score.eeee swwbazz_napieee
fg3a 3Pt,
{C batta.bx__sco www.sscrrr.saaa....ee. chabazz_napierr ftm })-(( data.box_ box_score__eeooeoeoooooooooooeee shwbazz__appeee
fta }) FT) to go with (( data, dataabbo_score.shhaaaaaaa swabazz_napperrreet }} rebounds,
(( data. www_score. sscore.shabazz_napier st1 }} steals and
{( data. box_score www.ssh_haaaaiiiaa aaaapierastt ast }) assist. Miami has quick turnaround, as
they take the Charlotte Homets at home Sunday night. The Magic just couldn't
get enough production outscore the Heat on this night, as only three players hit
double digits, with (( data. box box_boo__ooooooo www_s.cre... nikola, wwwaavuuevicccc player_name }} having a monster night, scoring {( data. baaabboosccrreee nikola_ wucevvic_cccccccpccmaaa pts }} points (C( data.
box_score. nikola. vucevic. ))-(( data. data.box_se...eeeaeeeeeeeeeeaeet bww_scooe.eooeeeeoooooooooooeee. wwkoor..ikklaaa..... Milooalvueeiiccccc sireviccfee fga }} FG,
{( data. bow_score.ee.occcoooooooco.oeeee wwwore..kkolaa.........o..o...aa sikkol__vuceiiee ftm })-(( data. ccore.nikola. wwwolallaaa.ooo vucevic..
fta }} FT), with data.box_scee wwccore..ikoaa..... wikola_vucevic....mm }) rebounds. Vucevic was
just dominate, and grabbed (( data. bow.bboo_scrrrcc www.ar..iikla..uceeiiiio oreb }} offensive
40

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boards the night. The other production came from
{( data. box_score. victvoi-irree _otttoooooopoo.... player_name }), who had
{( data. box_score. wwcttr_olaadiiieeeo .ttsippppsss............. }] points (C( data. box_sorox.cor...
dictor_oladipo fgm })-{{ data. bco_score, victor_olan _ottor_oladpo.... fga }} FG,
{( data. box_score victor_oladipo. fg3m })-{( data. box_score. wwwtoo_ooaaiiiipo./.
fg3a }} 3Pt,
{( d box_score. victor_ooadiiee ftm 3)-(( data.box box_score _octor_oladipo..
fta }) FT), with {(
wwteeeicccooooadipppooo reb }} rebounds,
{( data. box_score www.ssrr...iitrr..eeee
victor_oladipo .st }} assist and
data. box_score wow.score..icctrroolloo..... wwwcooioooaaiioooo.....oo
}} steals, well
{( data. box_score. www.scoreeeean_fourriiii
{( data. data.box_scoo evan_fournier. wwtatpppaaaaaaaaee
}), who scored evwn__ournierr pts }) points ((( data. box_score. wwan_fournier: fgm })-(( data.box_score.eveaa eoan_furnierrr
}} FG, www.:oooreeeeaooooeeeeeoe fg3a }} 3PT) with { data. box_bbox_rrooooeeeeeeeeeeeeeeoee score. frrr__fu ast }} assists and
{( data. box_score. evan_fournier. fgām })-((
{( data. box_score. eaarre.eva__aai.. avan_fournier reb }} rebounds. The Magic get a night off now,
before taking on the Cleveland Cavaliers on Monday.
User:
Data for game to be described
41

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EXAMPLE INPUT
JOON:
Joon
data
Tine""ine":
"aaee" "Hawks".
"city"
"Atlanta".
"wins" " 46.
"pts"" 95,
"pts_atri": 28.
"pts_atr2": 14.
"pts_qtr3": 34.
"pts_atri" atre"; 19.
"fg_pet": 11.
"fg3_pet": 45,
"ft_pct::
"ast": 27.
"reb": 42.
"tov":
"yisiter line": [
"nane" "Magic",
"city". "Orlando", "wins":
"lesses": 41.
"pts": 28,
"pts_atri": "pts,
"pts_atr3": 22.
"pts_atr4"; 21.
"fg_pet":
"rg3_ pct": 35.
"ft_pct" 54.
ast 22.
"reb":
_ox_score";
"tobias_ harris"
"alayor_nane""
Harris",
"First_name" "Tobias". "second_nane" "amedd_amee" "Harris", "city": "Driando" "scart_position" "p". "ein": 37.
"pts": 16, "fge":
"rga":
"fg_pct": 56.
"1g3n": 2.
"fgaa":
"fg3_pct";
*fte": 3,
"fta": 4.
"re_pct";
"oreb": 6,
"dreb":
"ast":
"bik": 1
"to": 2.
"pr":
Write summary the described above.
Assistant:
LLM generates summary without symbolic references.
42

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Published as a conference paper at COLM 2024
EXAMPLE OUTPUT (GPT-4-32K)
(46-12)
Magic (19-41) 95-88 homee:
strong performance
third quarter, scoring 34 points, which helped them secure the victory. The Hawks were led by Paul Milkap, who scored 20 points, and Al Horford, who added 17 points and gribbed 13 rebounds. Jelf Teagu also contributed with 17 points and other hand, the Magic rebounds. Victor Oladipo also contribused with points, while Tobias Harris added points. Despite loes, the Magic outperformed the Hawks the second quarter, scoring 29 points.
led Nikola Vucevic, who had strong performance with 21 points and 15
User:
Your job is to now rewrite the answer you provided above, but instead of responding directly with the text, you must generate Jinja-style expressions the text with references fields the provided JSON, egg, given JSON that says:
jssoon
"data": "place_of_birthh" "Ohio"
then instead of generating ".. born in Ohio" you should generate", "... was born in { data. wwwcc_aceoofooo _aac__o__birth )3". Note that you can only reference fields that exist the JSON. The user will be displayed version this message that has the references filled in, and you should ALWAYS try to use such references instead of copying the text directly when possible. Remember, you MUST make sure you ONLY use fields that exist in the JSON. You must NOT make up fields.
Assistant:
LLM rewriles summary to contain symbolic references.
EXAMPLE OUTPUT (GPT-4-32K)
The (C data. hore, www..iine.cc. 13 data, hove.
hine Line nane 33 data, hore, Line.wins 13-[1 hove.hov___nn Line. losses )1)
defeated the
visitor__ 1inr__nneeetttt
data })-(( data, visiloo
[[[ visitor
13) ( data, hore_lineepts })-(( data, wistvviiitorrrrnio pts ))
at home. The Hawks had strong performance in the third quarter, scoring (C data. home_line sine_.iieeptsettt pts_etr3 3) points, which
helped them secure the victory. The Hawks were led by data box score. scoro.paul_eii Leilspp player_nave
points, and data, box_score,
who scored
alrorfordd player [[ data bos_score al_horford, pts points and grabbed (( data. score here horrforrd
gaul_nillsap.
added }) rebounds.
[[ data box score. toague player_nae name }) also contributed with (( data box score,
www.e.jeff.f......oo jaff_teagun pts }) points and assists. On the other hand, the Magic were led by www.lo..vceeiiccccooooo.o player_nase
data box www.scareee..o..ooooooooooeeeeee feffffffftttt jewfffttggue......
strong performance
bor__coree wateeennklllaveeeeoo vucele, pts points and
acore. nikala vucavie... }) rehounds.
[( data score vietor_cladipo, player_nare name )) also contributed with (E data, box, score, victor. ola.ooo..ooo... pts 3) points, while (I data, box, score. tobi Farris, player_name added ( data, boorec tobi harris points. Despite the Magic oulperformed the Hawks
quarter, scoring
visitor pts_ttrt points.
I.2.3 Obituary prompts
Obituary prompt (Baseline)
System prompt:
Your task is to write an obituary about a person whose description is given in JSON. You must mention and be faithful about every property of the person in your obituary.
Few shot examples (optional):
User:
JSON:
json
(
"data": {
"name": "Niels Henrik Abel",
43

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"age": 27,
"place_of birthh" "Nedstrand", Delace_of_death". "Froland", "father": "Sren Georg Abel",
"country.of.ciiizznshii""" "Norway", "educated_at": "University "Uniiriiiyy of Oslo", "calculus", "
"occupation": "mathematiciin""
"employer": "University of Oslo",
"religiion.o..wwrrdiieee "Lutheranism", "award_receivedd" "Grand prix des sciences
mathematiques"
"member_of": "Royal Norwegian Society of Sciences and Letters",
"cause_of_death" "tuberculosis", "residence": "Norway",
mootabbe_wwrk? "abelian group". "languages_spoker spoken"; "Norwegian"
}
Assistant:
Niels Henrik Abel, renowned mathematician, passed away at the young age of 27 Froland, Norway. Born in Nedstrand, Abel was proud Norwegian citizen and devoted Lutheran. He was educated at the University of Oslo, where he later worked professor. Abel made significant contributions to the field of calculus, most notably the concept of the abelian group. He was member of the Royal Norwegian Society of Sciences and Letters and received the prestigious Grand prix des sciences mathematiques. Sadly, Abel's life was cut short due to tuberculosis. He be remembered for his remarkable achievements and dedication to the world of mathematics.
One more few-shot example like the one above (hidden to save space)
User:
Data for obituary to be generated
EXAMPLE INPUT
JSON:
jkcn
data":
"nare": "Barbert Spencer",
'age":
'place_of_birth"" "Derby", 'place_of death"" "Brighton",
'father': 'Villian George Spencer". 'country kcountry.of.ciiizemsii 'field "botany".
"United Kingdon of Great Britain and Ireland",
"eccupation" "writer".
'eaployer": "The Fcononist", oflace.of.burial. "lighhate temetery".
ceciziion.or.wordviiii " "agnosticisn"" "renber_or"" "Athenseur club", inotable_work" "Social Statics". 'noninated_for" "Nobel Peace Prize',
"languages_spoken spoken": "English"
Assistant:
Generate untemplated obituary. I.I.M generates obituary without symbolic references
44

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EXAMPLE OUTPUT (GPT-3.5-4K)
Spences, distinguished writer bolanist, passed away age Brighton. Geonge Spencer, he was citizen of the United Kingdom of Great Beitain and Ireland. Spencer made significant contributions to the field of bolany and worked writer for The Economist. He was member of the Athenaeum Club and was known for his fluent English. Although awarded, laid to rest Highgate Cemetery, leaving behind legacy of
Derby
notable work, 'Social Staties."
propenent agnoslicism
was nominated intellectal pursuits and contributions to the seientific community.
Nobel Peace Prize Spencer
Obituary prompt (Direct)
System prompt:
Your task write an obituary about a person whose description given in JSON. You must mention and be faithful about every property of the person in your obituary. Instead of responding directly with the text, you must generate Jinja-style expressions in the text with references fields in the provided JSON, e.g,, given JSON that says:
jjson
"data": @place_of_birte "Ohio"
then instead of generating ".. was born in Ohio" you should generate ".. born in { data. place_ ofrbit }3". Note that you can only reference fields that exist the JSON. The user will be displayed version this message that has the references filled in, and you should ALWAYS try to use such references instead of copying the text directly when possible.
Few shot examples (optional):
User:
JSON:
json
{
" "data":
"name": "Niels Henrik Abel", "age": 27.
"place_of_birth" "Nedstrand".
"place_of_death" "Froland", "father": "Sren Georg Abel", "contry..ofcciiienshipp" "Norway",
: "University of Oslo",
"fildd_f__work" "calculus",
"ocuppation" "mathematician"
"employer" "University of Oslo",
"religioonor_worlliiee "Lutheranism", "award_received": "Grand prix des sciences
mathematiques".
" "member_of"; "Royal Norwegian Society of Sciences
Letters".
"cause_of_deathh "tuberculosis",
"residence": "Norway".
"notable "orkk: "abelian group",
Sanguagess_spoentt "Norwegian"
Assistant:
45

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{( data. }), renowned (( data. occupation }), passed away at the young age (( data. age }} (( bata.place od__hhhhh }),
{( dat...cuuttr_.oom countryoon_ccocc taattrryoo__ciiiinsshiie
}). Born in { data. data.place_of bf }),
{{ data. name. split(' s ()[2] }} was proud Norwegian citizen and devoted Lutheran. He was educated at the {{ data. educated_at }), where he later worked professor.
data. name split( ')[2] }} made significant contributions to the field of ( data. field_of oflid_of____e _ork }}, most notably the concept of the fiea.
{( data. notable_word }}. He was a member of the {{ data. member_of }} and
received the prestigious {( data. award_ rereeveed }). Sadly,
data. name.
www..smlltee...................e split(' ')[2] }J's life cut short due
{( data. cause_of. _f__eath }). He will be remembered for his remarkable
achievements and dedication to the world of mathematics.
One more few-shot example like the one above (hidden to save space)
User:
Data for obituary be generated
EXAMPLE INPUT
JSON:
Jaon
"data":
"nare": "Borbert Spencer",
"age": 63.
"place_af_birth" "Derby",
"Flaco_of_death". "Brightan',
'father": 'Millian George Spencer',
"country_of_ere htt.rry_oo__ttteshipppppoo "United Kingdon of Great Britain and Ireland".
"fseid_of_work" "botany",
"eccupation" "writer".
"saployer": Fcononist",
'place. of_burial" ": "llighgate cemetery".
'religion_or con_or_verllvieee "agnosticisn". "senber_of":
club",
'notable. "Social Statics",
'nominated_for"" "Nobel Peace Prize",
"langugges_spoeen" "Faglish"
Assistant:
LLM directly genenates obituary using symbolic references.
EXAMPLE OUTPUT (GPT-3.5-4K)
nome 2),a prominent writer botaniss, has passed away at age {[ data age })
[1 data wwl.ce_oraa.......o.o.ooooa _e_tth }). Born in (( data pllac_of__brrte }) father (C data. father 3),
data nane. split(' '2[1] was cilizen of the [C data, couutrcuuoooooo ctt.fffcccttrtiipppp 1). He dedicated his life to the field of
worked
exployer [C
splieC 19013 })
(( data, nember 1] was nominated
naxinated_for }), was
[[ data. Languages__spot spokan )) and was known for notable work, "CC data, potable_work 3)."
data name, splii(" ')E3] 3)'s agnostic worldview shaped his
life and the world. He will he laid rest
}). contributions to field bolany and his literary works remembered
generations to come.
I.3
Obituary QA evaluation
Prompt for obituary QA evaluation
System prompt:
Your task answer questions about an obituary. Do not rely on any knowledge other than what is provided the obituary. You should state your answer without any explanation. The answer can be single word or a short phrase. The user message will be of the form:
46

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STORY:
<story>
QUESTION: <question>
You should reply only with your answer. If you do not know the answer, you should reply with 'Unknown.'
User: Question be answered, alongside story
EXAMPLE INPUT
STORY:
Herbert Spences, distinguusshed pharmacist and author of "Social Staties, has passed away at the age of 83 Vienna. Dorn in Derby, Spencer the Lord Byron citizen of the United Kingdom of Great Britain and Ireland. Although his field work primarily physies,
pharmacis and was a member the Athenacum Club and was known for his agnoalic worldview. He was nominaled for the Nobel Prize in Physialogy Medicine for his notable work. Spencer will be laid to rest Highgate Cemetery. He was fluent in Swedish and will he rememihered remarkable achievements the field of phannacy and his influential writings. Spenser
significant
Economist. Spencer
Assistant:
LLM generates answer to question based on provided obituary.
EXAMPLE OUTPUT (HAND-WRITTEN
PURPOSES)
Derby
1.3.1 Financial prompts
Financial prompt (Baseline)
System prompt:
Your task is to answer a finance-related question based on some company information given in JSON. You must remain remain faithful to the provided information in your answer.
Few shot examples (optional):
User:
QUESTION: How do the 50-d moving averages between AMZN and TSM compare?
JSON:
json
{
"data": (
AMZN": (
"Symbol" "AMZN"
"AssetType": "Common Stock" "Name": "Amazon. com Inc",
" "Description": "Amazon. com, Inc. is American multinational technology company which focuses on e-commerce, cloud computing, digital streaming, and artificial intelligence. It is one of the Big Five companies in the U.S. information technology industry, along with Google, Apple, Microsoft, and Facebook. company has been referred to as one of the most influential economic and cultural forces in the world, well as the world's most valuable brand." " "CIK": 1018724
"Exchange": "NASDAQ", "Currency": "USD", "Country": "USA",
47

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Sector "TRADE SERVICES",
Industry": "RETAIL-CATALOG MAIL-ORDER HOUSES",
"Address": "416 TERRY AVENUE NORTH, SEATTLE, WA, US"
"FiscalYearEnd"; "December", "LatestQuarter": "2023-09-38",
"Market(Gaittalzzttion" 1235158172900,
"EBITDA": 63431800000,
"PERatio": 94.9.
"PEGRatio": 11.85,
"BookValue", 16.35.
"DividendPershare": 0.0,
"DividendYield": 0.0,
"EPS": 1.26.
"RevenuePerShareTTh" 52.56,
"ProfitMargin": 0.0243,
"operatingkarriinTTN" 8.0572,
"ReturronnAssetsTTTM" 0.0247,
"ReturnoonEquityyTT"" 0. 0872,
"GrossProfiLTTMM
"DiLutedEPSTTM"" 1.26,
CuurtteriyEarrinngseroottt"o" -0.986,
"uarteeryyttveeeerrrrtttt""""""" 008,
"maalystTargetPriice: 160.61,
"TrailingPE": 94.9,
"ForwardPE": 43.86,
"FricceToSaleeRattiTTM"""" 1.797,
"FriceToBookPatio"; 6161,
"EVToRevenue": 1.948,
"EVTOEBITDA": 19.3,
"Beta": 1.188,
"52Weektigh": 145.86.
"52WeekLow": 81.43,
"50DayMorringAverasee": 132.31,
"200DayMooinnnveeragee"" 117.12,
" "SharesOutstandingg: 10317800000, "DividendDate": "None". "Ex0ividendDate"" "None"
"TSM":
" "Symbol", "TSM" "AssetType": "Common Stock",
"DividendDate": 2024-01-11"
"ExDividendDate": -2023-12- 11-1
Two more few-shot example like the one above (hidden save space)
User:
Question and data required to answer it.
48

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Published as a conference paper at COLM 2024
EXAMPLE INPUT
QUESTION: company largest market market capitliitionn,,
JSON:
* Jsen
"data"
"ASKL
Symbol "ASML"
"AasetType"; "Cormon Stock"
Hollingg
"Description", "ASML Hollinn develops, produces, rarkets, sells and services advanced seniconductor equipment systens consisting of lithography. netroicgy inspection related for and logic chip
Ranufacturers. company
Netherlanes.""
"CIK": $37966.
"Exchange": "MASDAQ".
"Currency": "USD",
"Country": "USA".
"Sector": "TECHINOLOGY".
"Industry": "SPECIAL INDUSTRY MACHINERY, NEC", VELOHOVEN,
"Aedress": "DE
"fiscalYearEnd": "Decenber",
"LatestQuarter". "2823-69-30".
"Mrrkectnpitslzzttin"" 248254151869.
"EBITDA": 5390500080.
"PERatLo":
"PEGRatio": 1.703.
"Boakvalue": 30.49.
"DividendPerShare""
"Dividendricld": 0.0111,
"EPS": 28.6.
"RevenuePerShareeTT" 1.224.
"ProfitMarein" 2.281.
"0peracin@*arstnTTT""" 8.327.
"Retura@nkssetsTTi"" 0.156.
"eturndnfqulyyTTT" 0.763,
"RevenueTTH" 25751701088
"GrossPoffitTt"" 1870010080e.
"'llutedFPSTIN" 20.6.
BuarteriySarnnnggrrorttoo 0.121.
"garteriuheveenurrootttor 8.155.
AaalysstarreePrico". 638.53,
"TratlsnzfE": 28.33,
"ForwardPE": 30.49.
"r/itaToSalesaaiioTtt"
"PriseTaBookRatto" 29.61.
"FVToRevarue" 12.62,
"EVTOESITDA": 35.36.
1.162.
"52KeekHigh": 778.52,
"52keekLow": 434.41,
"50bymoovinskveraa""" 615.52.
"28baythxvingvvrrag""
"haaesSutssanddin 303438888.
"Dividendbate". 2273--11-18"
"ExdividendDate" 22825-11-81"
"006""
"Symbol" "COOG"
"AssetType": "Cormon Stock",
"DividendDate": "None"
"ExDividendDate": "None"
2..
Assistant:
LLM generates answer without symbolic references.
EXAMPLE OUTPUT (GPT-4-324 3-KK)
company largest capstalization Alphabet (GOOG) capitalization 1,396,73,9926000 USD.
49

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Published as a conference paper at COLM 2024
Financial prompt (Direct)
System prompt:
company information given in JSON. You must remain remain faithful to the provided information your answer. Instead of responding directly with the text, you must generate Jinja-style expressions in the text with references to fields in the provided JSON, e.g, given JSON that says:
Your task is to answer finance-related question based
j*Jjson
"data" aplace_of_biree "Ohio"
then instead of generating ... was born in Ohio" you should generate "... was born {{ data. place_lcceoo r_brrth })", Note that you can only reference fields that exist in the JSON. The user will be displayed version of this message that has the references filled in, and you should ALWAYS try to such references instead of copying the text directly when possible.
Few shot examples (optional):
User:
QUESTION: How the 50-d moving averages between AMZN and TSM compare? JSON:
json
(
"data": {
AMMN" {
"Symbol" "AMZN"
"AssetType": "Common Stock", "Name": "Amazon. com Inc",
"Description": Amazon. com, Inc. is American multinational technology company which focuses on e-commerce, cloud computing, digital streaming, and artificial intelligence. It is one of the Big Five companies in the U.S. information technology industry, along with Google, Apple, Microsoft, and Facebook. The company has been referred influential ecanomic and cultural forces in the world, as well as the world's most valuable brand."
as one of the most
" "IKK: 1018724
"Exchange": "NASDAQ",
"Currency": "USD",
"Country" "; "USA".
"Sector": "TRADE SERVICES". " "Industry": "RETAIL-CATALOG MAIL-ORDER HOUSES",
"Address": "410 TERRY AVENUE NORTH, SEATTLE, WA, US"
"FiscalYearEnd": "December", "Latestquarter": "2023-09-38"
"MarketCapitalizaatinn" 1235158172000,
"EBITDA": 63431800000.
"PERatio": 94.9,
"PEGRatio". 11.85,
"BookValue", 16.35,
"DividendPer-haree" 0.0,
"DividendYield": 0.0,
"EPS": 1.26.
50

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Published as a conference paper at COLM 2024
'RevenuePerShareTTM:: 52.56,
"ProfitMargin": 0.0243,
"QperatinggarsinTTM"" 8. 0572,
"ReturnOOAAssessTTMM 0247,
"ReturnoOnEquityTTi:: 0. 0872,
"RevenueTM"" 5384400000
"GrossProfiLTTM" 225152000000,
"DiLutedEPSSTM"" 1.26,
QuarteenlySsrrinnggroottt"""""""
-8. -8.986,
Quartteri//evenuerroowttt""""""" 0.108,
"AnalystTarggePriie"" 160.61,
"TrailingPE": 94.9,
"ForwardPE" 43.86
" 1.797,
"FicceoBookRatio"; 6.61,
"EVToRevenue": 1.948,
"EVTOEBITDA": 19.3,
"Beta": 1.188.
"52Weektigh" 145.86.
=52WeekLow": 81.43.
"50DayMoviiggAeeragee" 132.31,
"200DayHowvnngvverge""" 12,
" "SharesOutstanding"" 10317800000,
" "DividendDate": "None", "Ex0ividendDate"" "None"
"TSM" {
" "Symbol", "TSM" "AssetType": "Common Stock",
"DividendDate": "2024-01-11" " "ExDividendDate": -2023-12 11:23-1220 -1-
Assistant:
The 50-day moving average {( data. AMZN. Name }} (CC data AMZN.
Symbol })) A
{( data. AMZN "5DaayoovinAvverrgee ] }} USD, while for {( data. TSM Name }}
(CC TSM. T })) is [( data. TSMC indgyyoving }} USD.
Therefore, data. data.AMZN. Symbol ))'s 50-day moving average higher than
data. TSM.bool ))'s.
Two more few-shot example like the one above (hidden to save space)
User:
Question and data required to answer it.
51

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Published as a conference paper at COLM 2024
EXAMPLE INPUT
QUESTION: company largest market market capitliitionn,,
JSON:
* Jsen
"data"
"ASKL
Symbol "ASML"
"AasetType"; "Cormon Stock"
Hollingg
"Description", "ASML Hollinn develops, produces, rarkets, sells and services advanced seniconductor equipment systens consisting of lithography. netroicgy inspection related for and logic chip
Ranufacturers. company
Netherlanes.""
"CIK": $37966.
"Exchange": "MASDAQ".
"Currency": "USD",
"Country": "USA".
"Sector": "TECHINOLOGY".
"Industry": "SPECIAL INDUSTRY MACHINERY, NEC", VELOHOVEN,
"Aedress": "DE
"fiscalYearEnd": "Decenber",
"LatestQuarter". "2823-69-30".
"Mrrkectnpitslizttin"" 248254151869.
"EBITDA": 5390500080.
"PERatLo":
"PEGRatio": 1.703.
"Boakvalue": 30.49.
"DividendPerShare""
"Dividendricld": 0.0111,
"EPS": 28.6.
"RevenuePerShareeTT" 1.224.
"ProfitMarein" 2.281.
"0peracin@*arstnTTT""" 8.327.
"Retura@nkssetsTTi"" 0.156.
"eturndnfqulyyTTT" 0.763,
"RevenueTTH" 25751701088
"GrossPoffitTt"" 1870010080e.
"'llutedFPSTIN" 20.6.
BuarteriySarnnnggrrorttooo 0.121.
"garteriuheveenuerootthhr 8.155.
AaallsstarreePrico". 638.53,
"TratlsnzfE": 28.33,
"ForwardPE": 30.49.
PrrieeToSalesatiooTee
"PriseTaBookRatto" 29.61.
"FVToRevarue" 12.62,
"EVTOESITDA": 35.36.
1.162.
"52KeekHigh": 778.52,
"52keekLow": 434.41,
"50bymoovinskveraa""" 615.52.
"28baythovingvvrrag""
"haaesCutssanddin 303438888.
"Dividendbate". 2273--11-18"
"ExdividendDate". 22825-11-81"
6006""
"Symbol" "COOG"
"AssetType": "Cormon Stock",
"DividendDate": "None"
"ExDividendDate": "None"
2..
Assistant:
LLM generates answer directly with symbolic references.
EXAMPLE OUTPUT (GPT-4-32M) 3-3k):
company largest capitalization CC data. (CC ssta. Symbol 31) copitalization (C data, 0006, Marke
USD.
52

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Published as a conference paper at COLM 2024
Financial prompt (Indirect)
System prompt:
Your task is to answer finance-related question based company information given JSON. You must remain remain faithful to the provided information your answer. After you generate the answer, you will be asked to rewrite it using Jinja-style expressions.
Few shot examples (optional):
User:
QUESTION: How do the 50-d moving averages between AMZN and TSM compare?
JSON:
json
"data": {
"AMZN": (
"Symbol": ": "AMZN",
" "AssetType": "Common Stock", " "Name": "Amazon, com Inc",
"Description": Amaaoon com, Inc. is an American multinational technology company which focuses on e-commerce, cloud computing, digital streaming. and artificial intelligence. one of the Big Five companies information technology industry, along with Google, Apple, Microsoft, and Facebook. The company has been referred influential economic and cultural forces the world, as well as the world's most valuable brand."..
is
the U.S.
of the most
" "CIK": 1018724,
"Exchange": "NASDAQ",
"Currency": "USD",
"Country" "USA".
"Sector": "TRADE SERVICES", " "Industry":
MAIL-ORDER HOUSES", " "Address": "410 TERRY AVENUE NORTH, SEATTLE, WA, US"
"FiscalVearEnd": "December", "LatestQuarter"" "2023-09-38"
"YarketCCaitaalzzttonn" 1235158172900.
"EBITDA": 63431800000.
"PERatio": 94.9,
"PEGRatio": 11.85,
"BookValue": 16.35,
"DividendPerShare". 0.0,
"DividendYield": 0.0,
"EPS": 1.26
"RevenuePerShareeTTM. 52.56,
"ProfitMargin" 0.0243.
" 0.0572,
"ReturnOnAssetsSTTM" 0.0247,
"ReturnonEQqiityTTM": 0, 0872,
"RevenueTTM" 5384440000000
"GrossProfittTI" 225152000000,
"DiLutedEPSTTM" 1.26.
QuartterlyaarrinngGrrotttt"""""" -0.99.
"garrterllfeveeudrroottttt""""" 0.188,
"AnalystTargetPrice"" 160.61,
"TrailingPE": 94.9.
"ForwardPE": 43.86.
53

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Published as a conference paper at COLM 2024
"FricelosaaesRatiiiTI""""" 1.797,
"riceTTBookRatioo; 6.61,
"EVToRevenue": 1.948,
"EVToEBITDA": 19.3,
"Beta": 1.188,
"52WeekHigh": 145.86,
"52WeekLow": 81.44.
"#0DayWovvingAveragge"", 132.31,
"200Dayooving@verree"""" 117.12
" "Shares@Outstanding"" 10317808000,
"DividendDate": "None", "Ex0ividendDate"" "None"
"TSM":
"Symbol" "TSM",
"AssetType": "Common Stock",
...
"DividendDate": 2024-01-11" " "ExDiviidendaate:: -2023-12 1-"14"
}
}
Assistant:
The 50-day moving average for Amazon.com com Inc (AMZN) 132.31 USD, while for Taiwan Semiconductor Manufacturing (TSM) it is 90.20 USD. Therefore, AMZN's 50-day moving average is higher than TSM's.
User:
Your job now rewrite the answer you provided above, but instead responding directly with the text, you must generate Jinja-style expressions in the text with references to fields in the provided JSON, e.g., given JSON that says:
jssonn
{
"data" {
opaace_o_bbrrth". "Ohio"
then instead of generating "... was born in Ohio" you should generate "... was born {( data. data.place_o. sacc__o__iireeeeeeeeeeeeeeeeeeed bfbitth }3". Note that you can only reference fields that exist in the JSON. The user will be displayed a version of this message that has the references filled in, and you should ALWAYS try text directly when possible. Remember, you MUST make sure you ONLY use fields that exist in the JSON. You must NOT' make up fields.
such references instead of copying the
Assistant:
The 50-day moving average for data. AMZN. Name (C( data AMZN. Symbol }}) is (( data. 550DattovinAAverraee
}} USD, while for {( data. TSM. Name }
(CC data. TSM...TMM... Symbol })) it is [( data. TSMC "50DayMovingAverage"" }} USD.
Therefore, {f data. data.AMZN, AMZN.Symbol ) 50-day moving average higher than
[[ da... Tymbol ))'s.
Two more few--sho example like the above (hidden to save space)
User:
Question and data required to answer it
54

---

Published as a conference paper at COLM 2024
EXAMPLE INPUT
QUESTION: company largest market market capitliitionn,,
JSON:
1 Jsen
"data"
"ASKL
Symbol "ASML"
"AasetType"; "Cormon Stock"
Hollingg
"Description", "ASML Hollinn develops, produces, rarkets, sells and services advanced seniconductor equipment systens consisting of lithography. metroicgy inspection related for and logic chip
Ranufacturers. company
Netherlanes.""
"CIK": $37966.
"Exchange": "MASDAQ".
"Currency": "USD",
"Country": "USA"
"Sector": "TECHINOLOGY".
"Industry": "SPECIAL INDUSTRY MACHINERY, NEC", VELDHOVEN,
"Aedress": "DE
"fiscalYearEnd": "Decenber".
"LatestQuarter". "2823-69-30".
"Mrrkectnpitslzzttin"" 248254151869.
"EBITDA": 5390500080.
"PERatLo":
"PEGRatio": 1.703.
"Boakvalue": 30.49.
"DividendPerShare""
"Dividendricld": 0.0111,
"EPS": 28.6.
"RevenuePerShareeTT" 1.224.
"ProfitMarein" 2.281.
"0peracin@*arstnTTT""" 8.327.
"Retura@nkssetsTTi"" 0.156.
"eturndnEqulyyTTT" 0.763,
"RevenueTTH" 25751701088
"GrossPoffitTtH" 18780180830.
"'llutedFPSTIN" 20.6.
buarteriySarnnnggtrorttooo 0.121.
"earteriyseeveeeeSrrthhhr 8.155.
AaaalsstarrgePricca. 638.53,
"TratlsnzfE": 28.33,
"ForwardPE": 30.49.
PrrieeToSalesatiooTee
"PriceTaBookRatto" 29.61.
"FVToRevarue" 12.62,
"EVTOESITDA": 35.36.
1.162.
"52KeekHigh": 778.52,
"52keekLow": 434.41,
"50bymoovinskveraa""" 615.52.
"28baythxvingvvrrag""
"haaesSutssanddin 303438888.
"Dividendbate". 2283--11-1"
"ExdividendDate" 22825-11-81"
6000""
"Symbol" "COOG"
"AssetType": "Cormon Stock",
"DividendDate": "None"
"ExDividendDate": "None"
2..
Assistant:
LLM generates answer without symbolic references.
EXAMPLE OUTPUT (GPT-4-322 3-KK)
company largest capitalization Alphabet (GOOG) capetalittoo 1,39,753,2266000 USD.
User:
Your job is to now rewrite the answer you provided above, but instead of responding directly with the text, you must generate Jinja-style expressions in the text with references fields in the provided JSON, e.g., given JSON that says:
55

---

Published as a conference paper at COLM 2024
jsJsson
"data": { "place_of_birth"" "Ohio"
then instead of generating ... was born in Ohio" you should generate "... was born in { data. place_of of_rithhh }}". Note that you can only reference fields that exist in the will be displayed version of this message that has the references filled in, and you should ALWAYS try use such references instead copying directly when possible. Remember, you MUST make sure you ONLY use fields that exist in the JSON. You must NOT make up fields.
JSON. The
Assistant:
LLM rewriles answer using symbolic references.
EXAMPLE OUTPUT (GPT-4-32K)
The with the largest market capitalizattinn is {( data, 600G. Name 33 (CC data, aita..000... Synhol 3)) with market capitalization (( data. Cocc, Marka tapttalaatll USD.
L.3.2
GSM8K prompts
GSM8K prompt (Direct)
System prompt:
You are helpful assistant.
Few shot examples (optional):
User:
We are working on solving math question. We want to generate the computation steps in jinja templating format along the way. We allow using "(C and "} to quote variables, and (x set variable=value x) to set variables. We do NOT allow using setting unknown variables (e.g., X) to value, and the computation should be simple possible.
Let's do practice round.
Assistant:
Sounds great!
User:
Question:
There 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will 21 trees. How many trees did the grove workers plant today?
Assistant:
There are 15 originally unitial_trees X). Then there were 21 trees after some more were planted set final_trees tree_trees- 21 %). Therefore, there are (% set trees_planted trees planted today.
final_trees mitial_trees X) trees_planted }
Answer: {( trees_planted }}
User:
Question: there are cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?
56

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Published as a conference paper at COLM 2024
Assistant:
There are originally 3 cars(* set initial_cars = 8). 2 more cars arrive(% set arrived_care (& set final_cars initial_cars arrived_cars x){( final_cars }}. Answer: {( final_cars }}
The total car in the parking lot
User:
Question: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces they have left total?
Assistant:
Originally, Leah had 32 chocolates (8 set raah_chocllates = 32 %), and her sister had 42(% set
42 %). In total, they had eeah_chocolate,
set chocllatos cotal_ccocolates }} chocolates. After eating 35(% set chocolates_ eaten 35 %),
kister_chocolale %){
they had (x set chocolates_lef- total_chocolates chocolates_eaten %)(( chocolates_left }} left.
Answer: chocolates_lett }}
User:
Question: Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?
Assistant:
Jason started with lollipops(* % set ini initial_lollipop after giving some to Denny. Now Jason has 12 lollipops(* X set folol_lollipops 12 %). So he gave Denny (% set lollipops_ghe bollipops_give }}. Answer: {( hollipops_given }}
%). Then he had 12
mnitial_lollippe
lollippos *)C
User:
Question: Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?
Assistant:
Shawn started with to set initial_ toys 5 8). He got 2 toys each from his mom and dad, that's (% set new_toys 2 + 2 (){( new_toys }} more toys. So he have (% set toys_now initial_too toys now_toys x){ toys_now toys.
Answer: {( toys_now }}
User:
Question:
There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers now in the server room?
Assistant:
There were originally 9 computers(* set initial_computers = 9 %). For monday to thursday (% set days 4 %), more computers were
added(% set daily_added _erpppuuedeec (% artal_aadddd_oomputess
5 8). Therefore, total of
faily_added_comppee
days K)C
ootal_added_cooputtee }} is added. There are (% set cotal_computers
+ initial_computer X)((
}} in the server room?
Answer: fetal_computers }}
User:
57

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Published as a conference paper at COLM 2024
Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have the end of wednesday?
Question:
Assistant:
Michael started with 58 golf balls set initial_golf_ballee gaalll = 58 %). After losing 23 tuesday(X x lost_ tuesday 23 %), he had (% golf half_balls__ee left initial wednesday(K set lost_ wed_eseayy
critial_gooff___. holl_balls lost_ tuesday XX{ golf_balls_left }}. He lost 2 more on
%3, so he has (% set final_golf_bon holf_balls
polf_balls_le left post_wednesday *){ gonal_golfbbals }} golf balls.
Answer: {( final_golf fblls }}
User:
Question: Olivia has $23. She bought five bagels $3 each. How much money does she have left?
Assistant:
Olivia had 23 dollars(* x initial_money 23 %).
bagels(% set bagel_count
x) for dollars each(% set bagel_price %) will be (% set bagel_cost dollars. So she has (% set money_left = ini initial_money bagel_cost *){ money_left }} dollars.
bagel_price * bagel_count x){ total_bagel_cos }}
Answer: money_left }}
User: Question we want an answer for
EXAMPLE
Here is another one:
Quesiton: Janet's ducks eggs day, She three for breakfast every morning and bakes muffins friends every remainder the farmens' market daily for per duck egg. How much dollars make every day the farmers' market??
day with
Assistant:
L.M generates answer with symbolic references
EXAMPLE OUTPUT (CPT-4-8K)
Janet's
eggs per day total_rges K),
breakFast_.ngs
for muffins set nuffin, eggs 8). So uses
(% set used eggs breakfast.ngge nuffin, eggs R3CC used_eggs 3) eggs. The remainder is
market,
egps. She sells these the farmers' market for
eggil set esg_price N),
set narket_.ncone Ancone narket, eggs egg, price K)CC
dollars every day.
Answer: market_incone ))
58


Reviews:
[{"review_id": "r9DPhsHk9f", "reviewer": "Reviewer_XpW4", "summary": "The authors propose SymGen which provides symbolic references to the input structure (JSON) along with the generation. Two variants are proposed, direct and indirect.  The direct approach prompts LLM to generate text along with Jinja-like references in one go. The in-direct approach first prompts LLM to generate a text summary, that is subsequently transformed into text with symbolic references by prompting.\nThe experiments:  \n1. study changes in textual quality due to symbolic reference generation\n2. show that the annotations cannot be simply replaced by regex-based replacement.\n3. show the system improves the verifiability speed.\n4. show that SymGen can also be applied to QA on structured (JSON) data and reasoning setup.", "questions": "", "limitations": "", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> Rotowire is a difficult dataset and requires a lot of exact numbers replicated [but] the baseline results do not support this. \n\nRotowire is a challenging testbed due to the sheer size of the JSON data presented to the LLM (see App. H.2.2).\n\n> SynthBio has a simpler JSON compared to Rotowire, but the authors suggest [it] is slightly harder. It would be very helpful to have some error analysis [...]\n\nFor SynthBio, JSON values are not perfectly formatted, meaning that LLMs will either output grammatically incorrect text or have to perform post-processing on-the-fly via more complex templates, which increases the chance of errors. We will add some examples to the paper.\n\n> the indirect strategy does not work for GPT3.5 as it is weaker than GPT-4 [but why?]\n\nWe found that for many examples GPT-3.5 would ignore the instructions/in-context examples and directly output a response with symbolic references in the first step. Though the metrics were still reasonable (e.g., for SynthBio, BLEU was 32.67 for 0-shot and 33.10 for 2-shot), we omitted these results from the table since GPT-3.5 was not correctly executing the indirect strategy.\n\n> no details on how the parsing and representation would work [...] other than JSON. [...] limited description of the parsing stage\n\nIn short, we use an off-the-shelf Jinja parser. Further, we found our approach to work fairly well by simply adjusting the prompt as long as the data format and templating language are not too exotic (e.g., YAML and Python f-strings also work).\n\n> not obvious [...]  why the authors chose not to use the in-direct method [in] Table 2.\n\nSince the QA evaluation was particularly costly, we chose to focus on a single LLM for this dataset–GPT-3.5–for which the indirect strategy does not function properly.\n\n> For the QA experiments, the gap between GPT-3.5 baseline vs direct methods is huge compared to GPT4, and experiments in Table 1.\n\nWe found that GPT-3.5 struggled to do CoT-like behavior when operating in symbolic space. This is discussed in more detail in App. F (financial error analysis).\n\n> [the] regex-based baseline is not a fair comparison. A prompt-based baseline would be more adequate\n\nOur goal with the Regex baseline was to ascertain whether the benefits of SymGen generations can be obtained without using LLMs. There may be other ways of augmenting generations with information that may be useful for verifying their correctness, but we believe such a comparison is out-of-scope for this paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "I believe that such a system is a natural attempt towards this problem. That could mean that the learnings are benificial to the community. I also appreciate that the authors have conducted multiple experiments that answer a variety of questions.", "reasons_to_reject": "In general, there is no clear recommendation by the authors. I see many mixed results, which is fine, but the paper does not map it to clear insights or error analysis. \n\nTable 1 (a) and (b) have mixed results depending on the dataset. The paper claims:\n1. That rotowire is a difficult dataset and requires a lot of exact numbers replicated. However, the baseline results do not support this. \n2. SynthBio has a simpler JSON compared to Rotowire, but the authors suggest that biographical generation in symbolic space is slightly harder. It would be very helpful to have some error analysis to gain an understanding of inherent difficulty.\n\nThe authors claim that the indirect strategy does not work for GPT3.5 as it is weaker than GPT-4. Due to a lack of examples/comments, it is difficult to say why is that the case. It could be helpful if there were different and possibly open-source models used as well to be able to helpfully distill when this method works. Although I do acknowledge the GPU requirements issue here.\n\nThere are no details on how the parsing and representation would work for representation other than JSON. Hence, it seems that the proposed method is limited to JSONs. Furthermore, there is a limited description of the parsing stage itself apart from seemingly simple regex + replacement.\n\nIt is not obvious from the writeup why the authors chose not to use the in-direct method for the experiments in Table 2.\n\nFor the QA experiments, the gap between GPT-3.5 baseline vs direct methods is huge compared to GPT4, and experiments in Table 1. My assumption is that it could be due to shorter outputs. But the error rate is missing in this experiment which makes the results unclear.\n\nIn the related work section authors talk about LLM-based verification methods, without justifying the differences or why comparisons cannot be made. On a related note, and agreeing with the associated post-processing cost of prompting, regex-based baseline is not a fair comparison. A prompt-based baseline would be more adequate. An approach could be (only to cite possibilities): for each summary sentence, prompt which key-value pairs support the sentence? Or a prompt: where all sentences are passed as a list at once to make it comparable to indirect generation.", "questions_to_authors": "In section 3.1 : Why does it say \"incomplete JSONS\"?", "extra_scores": {}}, {"review_id": "dxLAVlg3gZ", "reviewer": "Reviewer_oZFv", "summary": "The paper presents a novel approach, symbolically grounded generation (SymGen), which enhances the capabilities of large language models (LLMs) for complex reasoning tasks. The authors demonstrate that SymGen can produce text with accurate symbolic references while maintaining fluency and factuality, which is a significant advancement in the field of natural language processing.\n\nPlease note that I doubt whether the font used in this paper aligns with the Call for Papers (CFP) guidelines for COLM.", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. Innovative Approach: The paper introduces a new method for text generation that significantly improves verifiability without compromising the quality of the text generated by LLMs.\n\n2. Empirical Evidence: Strong empirical results support the claims made by the authors, with improvements demonstrated across a range of data-to-text and question-answering experiments.\n\n3. Practical Application: The proposed SymGen method has practical implications for applications such as virtual assistants and knowledge bases, where the ability to generate verifiable text is crucial.\n\n4. Human Study: The inclusion of a human study adds weight to the findings, showing that the annotations provided by SymGen can streamline the verification process.\n\n5. Well-Structured Paper: The paper adheres to the COLM reviewing guidelines and is presented in a clear, accessible manner, making it a valuable contribution to the literature.", "reasons_to_reject": "1. Limited Dataset Scope and Insignificant Superiority: The evaluation is based on a small number of toy datasets, which may not fully capture the diversity of real-world applications. I expect to see the performance of the method on some more realistic and complex datasets, e.g., table reasoning, where the method may be superior.\n\n2. Complexity of Indirect SymGen: The indirect SymGen approach, while maintaining text quality, could be more costly and less reliable with certain LLMs, which might be a concern for practical adoption.\n\n3. Error Analysis: The paper could benefit from a more detailed error analysis, particularly in understanding the types and causes of errors in symbolic generation.\n\n4. Generalizability: It is unclear how well the proposed method would generalize to other types of structured data (e.g., Graph, Table, Databases, etc.) and language models, which is a key consideration for its broader acceptance.\n\n5. More than Verifiability: The authors emphasize the proposed method's verifiability. But what are the benefits? I am curious about future work. Does better verifiability make it easier for the LLMs to receive human feedback and evolve? Do the authors have more in-depth insights?\n\n6. No Training: The paper does not attempt to train LLMs to master the technique of generating symbolic references, which limits its impact.", "questions_to_authors": "Please see Reasons to Reject", "extra_scores": {}}, {"review_id": "fEgf8kIrzA", "reviewer": "Reviewer_oNbF", "summary": "The authors propose a new prompting technique to generate textual descriptions\ngrounded in structured data, e.g., a news blurb describing financial data or\nsports statistics. The key innovation appears to be the use of Jinja-like syntax\nto inline symbol references in the generated text. The authors evaluate this\napproach on several data sources: biographies from WikiData, sports data from\nRotowire and financial data from a financial API, where they observe that their\nproposed approach generates descriptions that are comparably fluent and grounded\nto a baseline approach. The key benefit however are the references which can be\nused to show readers links to the source data: through a user study, the authors\ndemonstrate that these links allow readers to verify the data presented in the\ntext more quickly and easily.\n\nFinally, the authors show two other ways in which the symbolic references could be useful:\nfirst to help language models override their priors with counterfactual data,\nand second as a way to help language models use symbolic expressions for math problems.\nUnfortunately, in both of these applications, the results are mixed and are not\nconsistently better than the baselines.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": true, "final_justification": "", "rebuttal": "We would like to thank reviewers for their effort and constructive feedback! We are encouraged that XpW4 and oNbF acknowledge our contribution and find our experiments comprehensive and convincing.\n\nGiven this paper is in the **LMs and interactions track**, we aim to tackle **the pressing problem of human verification of LLM generations**. We developed **SymGen to make it easier for users’ to verify an LLM’s generation** and evaluate its performance via multiple human studies. To thoroughly understand how SymGen works on different tasks, we also experiment to ensure **SymGen’s generation quality is on par with the baseline** on multiple tasks, and we **do not intend to claim that SymGen can improve generations** (e.g., better reasoning or grounded generation). \n\n---\n\nWe will release the obituary dataset. \n\n> Not improving groundedness\n\nWe don't intend to improve the groundedness of the generation but focus on whether SymGen can maintain the generation quality compared to the baselines.\n\n> referencing near-exact matches\n\nWe focus on end users verification of the generation; studying the references to near-exact matches is a first step to this problem and avoids other confounding factors like incorrect user judgment on similar answers. \n\n> the application has been proposed\n\nWhile data-to-text generation is well-established, to the best of our knowledge we are the first to focus on how the generation can be made easier to verify by users. \n\n> precision/recall of generated answers in counterfactual eval\n\nWe mentioned that SymGen tends to not generate the response more frequently if it doesn’t know the answer, and conjectured that SymGen may in a loose sense optimize for higher precision than recall of the answer. One possible explanation for this is that symbolic generation includes slightly less information than regular generation (esp. in the few-shot case with higher unknown rate), which leads to more incorrect answers (more questions were unanswered). The results in Table 6 can be construed as the precision of SymGen, but computing recall is non-trivial in our setup.\n\n> report statistic significance\n\nAs mentioned above, we focus on the human eval dataset in the rebuttal. We recruited 60 annotators for the human study, which is a reasonably large size for human evaluation of this kind. We also add standard error on the mean for table 7 below; note that our time, confidence, and effort results have non-overlapping error bars. https://imgur.com/a/5IWhJbn", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The paper is well-written, and is comprehensive, with quantitative and human\n  evaluations, some qualitative error analysis and detailed information on the\n  prompts used.\n- The main contribution of this paper appears to be showing that their prompt\n  strategy works in providing references to structured data. The authors\n  convincingly show that this makes it easier for users to verify the generated\n  text through a user study, though they do not show that it significantly\n  improves the groundedness of the generations.", "reasons_to_reject": "There are several dimensions along which this paper makes contributions, but\nunfortunately, I found it hard to find one in which it excels:\n\n- Application: The chief contribution of this paper appears to be generating\n  symbolic references along with text to help show those references to users.\n  While valuable, the application has been proposed several times in the\n  literature, and this paper limits its scope to referencing (near-)exact\n  matches from the source data. I think this too narrow, given the state of\n  language models today (e.g., the GPT3.5/4 models used in this paper).\n\n- Technique: While the paper provides a clever way to prompt language\n  models to generate text with grounded symbolic references, this technique isn't\n  particularly effective at improving the groundedness of the generations over\n  baselines (which are already very good). Furthermore, given the relatively\n  small evaluation set sizes, it is unclear if any gains are statistically\n  significant or not. \n\n- Data: the paper does generate its own synthetic counterfactual obituaries\n  dataset, but the authors do not really analyze this dataset or make it public.", "questions_to_authors": "- In the discussion of the counterfactual evaluation, you mention that some of\n  the difference in performance might be explained as a precision-recall trade\n  off. This seems easily quantifiable, have you measured precision/recall of the\n  generated answers?\n- It is great that you included dataset sizes in the appendix, but I think this\n  information belongs in the main paper. It is really hard to understand what's\n  a meaningful improvement without that information. Given the relatively small\n  sizes of the evaluation datasets, could you do a statistical analysis and\n  report which results are actually statistically significant?", "extra_scores": {}}]

Author rebuttal:

Meta-review: Overall:  This paper suggests an approach for data-to-text generation with symbolic references as an intermediate step that can be presented to humans to make it easier to verify the text. The approach is interesting and the paper is generally well-written with comprehensive experiments.  There was a common confusion amongst reviewers about some of the goals of this paper, especially regarding the comparison of quality in Section 3.  I think that this can mostly be fixed in revisions, but will require some significant rewriting.  I also would suggest that Section 4 should be expanded further since it seems like the main goal of this approach is to help humans with verification which is what’s investigated by the user study.

Strengths:
- Interesting prompting approach
- helping users verify information (annotators took less time, and felt more confident and easier to verify outputs)
- Adding the SymGen annotations doesn’t seem to degrade output quality by much and they show this on three datasets

Weaknesses:
- The goals and contributions of this paper seemed to have been a point of confusion for both XpW4 and oNbF, though they seemed more satisfied after reading the author responses. Authors clarified in the response that “developed SymGen to make it easier for users’ to verify an LLM’s generation and evaluate its performance via multiple human studies”.  Authors also did state this in the paper: that the “main goal is not to improve upon the baseline”.  But I think this needs to be emphasized more clearly in general. Since this is actually the main goal of the work, my suggestion to the authors is that they may want to condense section 3 and instead do more of a deep dive into section 4 with more analysis and visualizations about the human studies.  Note: this seems mostly a writing/clarity issue but may require bigger revisions.
- Some lingering questions about experimental setup, baselines, etc. (see Reviewer XpW4)  that need to be clarified within the paper.  ← Authors have offered to update many of these in their general comments at the top of the discussion section.
- Experimental results seem a bit mixed (brought up by XpW4) without clear conclusions. I think that this may be ok for the intended purpose of this paper, which as I understand it, is to show that these annotations help with verification without degrading the quality by much. But, again, the writing needs to be updated to make the goals of this exploration more clear.

[At least one review was discounted during the decision process due to quality]
Decision: Accept