Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Models

Authors: carina  kauf
emmanuele  chersoni
alessandro  lenci
evelina  fedorenko
anna a ivanova
Venue: EMNLP_2024_Workshop_BlackBoxNLP
Published: 2024-08-16 10:23:31

Abstract: Semantic plausibility (e.g. knowing that "the actor won the award" is more likely than "the actor won the battle") serves as an effective proxy for general world knowledge. Language models (LMs) capture vast amounts of world knowledge by learning distributional patterns in text, accessible via log probabilities (LogProbs) they assign to plausible vs. implausible outputs. The new generation of instruction-tuned LMs can now also provide explicit estimates of plausibility via prompting. Here, we evaluate the effectiveness of LogProbs and basic prompting to measure semantic plausibility, both in single-sentence minimal pairs (Experiment 1) and short context-dependent scenarios (Experiment 2). We find that (i) in both base and instruction-tuned LMs, LogProbs offers a more reliable measure of semantic plausibility than direct zero-shot prompting, which yields inconsistent and often poor results; (ii) instruction-tuning generally does not alter the sensitivity of LogProbs to semantic plausibility (although sometimes decreases it); (iii) across models, context mostly modulates LogProbs in expected ways, as measured by three novel metrics of context-sensitive plausibility and their match to explicit human plausibility judgments. We conclude that, even in the era of prompt-based evaluations, LogProbs constitute a useful metric of semantic plausibility, both in base and instruction-tuned LMs.

Main paper: 
 introduction: 
 Effective language use heavily relies on general world knowledge. To determine which sentence is the most appropriate response in a given situation, 1 Code and data are accessible at https://github.com/ carina-kauf/llm-plaus-prob.a language user often needs to establish whether the sentence (e.g., "The actor won the award") plausibly describes the world. In NLP, leveraging world knowledge is important both for specific tasks (such as information retrieval) and for general success of a language model during interactions with a user (such as establishing common ground).Language models (LMs) are well-positioned to acquire many aspects of general world knowledge by capturing distributional patterns in their training data (Elazar et al., 2022; Kang and Choi, 2023) . For instance, by observing that "actor" occurs more frequently with "award" than with "battle", the LM might implicitly learn that actors are more likely to win awards than battles. Thus, a simple word-incontext prediction objective can enable an LM to acquire vast amounts of world knowledge.We focus on one particular way to assess general world knowledge: estimates of sentence plausibility. Plausible sentences conform with world knowledge whereas implausible sentences violate it; thus, the ability to distinguish plausible and implausible sentences is an indicator of underlying world knowledge capabilities. Plausibility judgments can be tested using both single sentences (e.g., "The actor won the award" > "The actor won the battle") and setups where plausibility depends on the context of the previous sentences (e.g., "The girl dressed up as a canary. She had a little beak." > "The girl was cute. She had a little beak.").A quantitative metric that has been commonly used to evaluate world knowledge in LMsincluding semantic plausibility-are the log probability scores (LOGPROBS) of the output under the model. LOGPROBS are relatively easy to compute and constitute a direct measure of model behavior (as opposed to more implicit metrics such as decod-ing probe accuracy; Li et al., 2021; Papadimitriou et al., 2022) . However, LOGPROBS are sensitive to many different surface-level text properties, such as individual word frequency, output length, and tokenization schemes (Holtzman et al., 2021; Salazar et al., 2020; Kauf and Ivanova, 2023) . Furthermore, distributional patterns are subject to the reporter bias: people typically communicate new or unusual information rather than trivial or commonly known facts (Gordon and Van Durme, 2013) . Thus, the link between LOGPROBS and semantic plausibility is confounded by a variety of factors. The most common way to control for confounds influencing LOGPROBS is by leveraging the minimal pairs setup (Futrell et al., 2019; Warstadt et al., 2020; Hu et al., 2020; Aina and Linzen, 2021; Pedinotti et al., 2021; Sinha et al., 2022; Michaelov et al., 2023; Hu et al., 2024; Misra et al., 2024) and/or quantifying the effects of multiple contributing factors on the resulting score (Kauf et al., 2023) ,With the rise of instruction-tuned LMs (Chung et al., 2022; Touvron et al., 2023; Almazrouei et al., 2023; Jiang et al., 2023) , it has become possible to directly evaluate LM capabilities via targeted natural language PROMPTING (Li et al., 2022; Blevins et al., 2023) . Thus, we ask: is explicitly prompting instruction-tuned LMs for semantic plausibility judgments more effective than using LOGPROBSderived plausibility estimates? And how does instruction tuning affect the LOGPROBS estimates themselves?On the one hand, PROMPTING might provide a better estimate of plausibility by filtering out influences of extraneous factors not mentioned in the prompt. Furthermore, instruction tuning might diminish the influence of those factors even at the level of LOGPROBS themselves, leading instruction-tuned models to perform better under either metric. On the other hand, initial direct comparisons of LOGPROBS and PROMPTING measures on different linguistic/semantic knowledge datasets revealed that PROMPTING may, in fact, systematically underestimate the model's internal knowledge by requiring the models not only to solve the task, but also to correctly interpret the prompt and to translate their answer into the desired output format (Hu and Levy, 2023; Hu et al., 2024) .As access to LOGPROBS for newer models becomes restricted, it is important to understand what knowledge can be accessed, and what knowledge is inaccessible to the experimenter if PROMPTING is the only way to interact with LMs. In addition, some researchers reported that instruction tuning decreases the utility of raw LOGPROBS in domains such as confidence judgments (Tian et al., 2023) and prediction of human reading times (Kuribayashi et al., 2024) , a change that might or might not be compensated by superior PROMPTING performance and that needs to be acknowledged as the field is shifting toward instruction-tuned LMs.In this paper, we provide a systematic comparison of semantic plausibility estimates in instructiontuned LMs. We test LMs' knowledge of plausibility in single-sentence (Experiment 1) and contextualized scenarios (Experiment 2) and compare implicit (LOGPROBS-based) and explicit (PROMPTINGbased) plausibility judgments. We find that:1. LOGPROBS, while imperfect, are a more dependable measure of plausibility than naive zero-shot PROMPTING.2. Instruction-tuning does not drastically alter LOGPROBS-derived plausibility estimates, although in certain cases they might become less consistent with human plausibility judgments compared to base model versions.3. LOGPROBS can be used to effectively model the contextual plausibility of events and replicate key patterns of human plausibilityjudgment behaviors in both base and instruction-tuned LMs. 
related work: 
 Evaluating single-sentence plausibility in LMs.In Experiment 1, we evaluate plausibility estimates for single sentences describing common events (Table 1). To evaluate plausibility, scholars traditionally tested NLP models with sentence pairs from psycholinguistic studies that differ for their degree of semantic plausibility (e.g. The mechanic was checking the brakes vs. The journalist was checking the brakes, from Bicknell et al., 2010) : the models' goal is to guess which of the two sentences is the most plausible one (Lenci, 2011; Tilk et al., 2016; Chersoni et al., 2016 Chersoni et al., , 2019 Chersoni et al., , 2021)) . Pedinotti et al. (2021) and Kauf et al. (2023) specifically tested event plausibility knowledge in non-finetuned LMs. Pedinotti et al. (2021) showed that LMs achieve correlation with human judgments on par with or better than traditional distributional models. Kauf et al. (2023) showed that Transformer-based models retain a considerable amount of event knowledge from textual corpora and vastly outperform the competitor models (i.e., classical distributional models and LSTM baselines). Nevertheless, both studies show LMs' generalization capabilities to novel experimental manipulations of the target sentences are limited and that LOGPROBS are affected by task-irrelevant information, such as the frequency of words within a target sentence.Evaluating context-dependent linguistic judgments in LMs. In Experiment 2, we evaluate context sensitivity of LM plausibility estimates (Table 5 ). Initial work in this domain shows that LMs can modulate their probability estimates to accommodate a previously unlikely target word (e.g., A peanut falls in love) following a short licensing context (Michaelov et al., 2023; Hanna et al., 2023) , results that are consistent with human data (Nieuwland and Van Berkum, 2006; Rueschemeyer et al., 2015) . Nevertheless, probability-based judgments of LMs can also be adversely influenced by context, for example in cases where the context contains information that is not related to the task (for syntax: e.g., Sinha et al., 2022;  for factual knowledge: e.g., Kassner and Schütze, 2020) .Comparing LOGPROBS and PROMPTING. The direct interaction with LMs through natural language prompts is exciting for many reasons, including the ability to run the exact same experiments on models and on humans (Lampinen, 2022) . Nevertheless, Hu and Levy (2023) ; Hu et al. (2024) showed that the use of metalinguistic prompts for model evaluation may underestimate their true capabilities. They compared LMs' syntactic/semantic knowledge across four minimal sentence pair datasets and showed that, on aver-age, direct probability measures were a better indicator of these knowledge types than answers to prompts (similar to us, they used DTFit as one of their datasets, but their prompts did not explicitly probe the notion of plausibility; thus, we chose to include DTFit in this work; see Appendix §B, Figure 6 for a more direct comparison).Evaluating the alignment of instruction-tuned models with humans. Even though instructiontuning has been claimed to better align the representations of LMs and those computed by the human brain (Aw et al., 2023) , others show that it does not always help for the alignment at the behavioral level (Kuribayashi et al., 2024) . However, the work in this domain is still sparse.3 Experiment 1: Single-Sentence Plausibility JudgmentsIn this section, we test LMs' knowledge of semantic plausibility in isolated sentences. We compare implicit (LOGPROBS-based) and explicit (PROMPTING-based) judgments derived from the base and instruction-tuned versions of 3 state-ofthe-art LMs. We also compare LM scores with human plausibility judgments. 
datasets: 
 We use two curated sets of minimal sentence pairs (n ∼ 2000 overall) adapted from previous studies (for an overview, see Table 1 ):EventsAdapt. The EventsAdapt dataset (Fedorenko et al., 2020) is composed of 391 items, each of which includes (i) a plausible active sentence that describes a transitive event ("The teacher bought the laptop"), (ii) the implausible version of the same sentence, constructed by swapping the You will be given a sentence. Your task is to read the sentence and rate how plausible it is. Here is the sentence: The nanny tutored the boy. How plausible is this sentence? Respond with a number on a scale from 1 to 7 as your answer, with 1 meaning "is completely implausible", and 7 meaning "is completely plausible". Answer: { 7, 6, 5, 4, 3, 2, 1 }  Sentence Judgment Here is a sentence: The nanny tutored the boy. Is this sentence plausible? Respond with either Yes orNo as your answer. Answer: {Yes, No} noun phrases ("The laptop bought the teacher"), and passive voice alternatives ("The laptop was bought by the teacher" and "The teacher was bought by the laptop"). The items fall into one of two categories: a) animate-inanimate items (AI ; "The teacher bought the laptop"), where the swap of the noun phrases leads to impossible sentences; and b) animate-animate ones (AA; "The nanny tutored the boy"), where role-reversed sentences have milder plausibility violations. Given these differences, we model the two subsets independently.DTFit. The DTFit dataset (Vassallo et al., 2018) contains 395 items, each of which includes (i) a plausible active sentence that describes a transitive event ("The actor won the award"); (ii) a less plausible version of the same sentence, constructed by varying the inanimate sentence patient ("The actor won the battle").For DTFit, participants answered questions of the form "How common is it for a {agent} to {predi-cate} a {patient}." (e.g. "How common is it for an actor to win an award?") on a Likert scale from 1 (very atypical) to 7 (very typical) (Vassallo et al., 2018) . For EventsAdapt, participants evaluated the extent to which each sentence was "plausible, i.e., likely to occur in the real world" on a Likert scale from 1 (completely implausible) to 7 (completely plausible) (Kauf et al., 2023) . For each sentence, we average judgments across the human participant pool to obtain a single score.Models. We test the base and instruction-tuned versions of three popular autoregressive LMs: (Almazrouei et al., 2023) , and MPT (MosaicML NLP Team, 2023), all of them with 7B parameters.Mistral (Jiang et al., 2023), FalconMetrics. We evaluate LMs using (i) LOGPROBS and (ii) several zero-shot PROMPTING methods (Table 2 ) (Hu and Levy, 2023) . LOGPROBS are calculated as the sum of the log-probabilities of each token w i in a sentence, conditioned on the preceding sentence tokens w <i . In our main analysis, we evaluate LMs using four natural-language prompts (Sentence Choice I/II, Likert Scoring and Sentence Judgment; Table 2 ). These prompts were designed to explicitly query the LMs' knowledge of sentence plausibility and use either the same or similar instructions to the task that humans solved (see §3.2).2 For all prompting methods except Likert Scoring, we compare the probabilities that models assign to ground-truth continuations (in green) over implausible continuations (in red). For Likert Scoring, we ask models to generate a number from a constrained set of answers, using the outlines Python library3 , and compare the generated scores for plausible vs. implausible sentences (the results remain consistent across free vs. constrained generation prompting, see SI §C, Figure 7 ). In our main experiment, all prompts are framed using the direct plausibility query "is plausible". Supplementary analyses show that this pattern of results remains consistent for alternative queries of plausibility, such as "makes sense" (SI §C, Figure 8 ) and "is likely" (SI §B, Figure 6 ).Binary accuracy. For each item, we compare the scores/generations of the minimally different plausible and implausible sentence conditions, and compute the binary accuracy as the ratio of dataset items in which the LM/the human subject pool assigns a higher score to the plausible vs. the implausible sentence variant. The chance level is 50% for all benchmarks except Sentence Judgment, where, following Hu and Levy (2023) , we compare the models' propensity to output the ground truth answer in both plausible and implausible settings, leading to a chance performance of 25%.Result 1: LOGPROBS results are consistent across models, whereas PROMPTING is hit-or-miss. Across model architectures and plausibility datasets, LOGPROBS are an effective estimate of plausibility knowledge in both base and instructiontuned LMs (Figure 1 ). Overall performance patterns across datasets-DTFit; EventsAdapt, AI; and EventsAdapt, AA-are consistent across models, with only minor performance differences. The re- sults are also consistent with prior work (Kauf et al., 2023) , showing a performance gap between AI sentences (easier) and AA sentences (harder). PROMPTING the LMs with our queries, by contrast, yielded inconsistent results. While Mistral showed above-chance performance for several prompts, Falcon and MPT performed at chance level for all prompts tested (for complete prompting results, see SI §A, Figure 5 ). Interestingly, even the base Mistral model performed above-chance on some prompts (Sentence Choice I), suggesting that model pretraining and/or architecture may be important for the prompt to work in an instructiontuned model.Prompts can be tuned to work well for a specific LM and task (Qin and Eisner, 2021; Pryzant et al., 2023; Chen et al., 2024) . Even though we do not explore automatic prompt-optimization approaches in this study and instead test variations of the naturallanguage prompt that humans saw during the experiment (and which people interacting with these models may plausibly use when querying for semantic knowledge in LMs), we observed that certain ⟨prompt,model⟩ combinations indeed led to improved performance over LOGPROBS (Table 3 ). Despite this success, however, our comparison critically shows that the same prompt that is effective at tapping into plausibility knowledge in one model class (i.e., Sentence Choice I for Mistral models) need not be effective in tapping into the same knowledge in other models (Figures 1, 5 ). Likewise, we show that the same model that exhibits successful task performance when prompted in a certain way can exhibit poor performance when queried with slight variations on the same prompt (e.g., Table 3 ; see also Sclar et al., 2023) . This brittleness of PROMPTING-based evaluations stands in contrast to the robustness of the model-agnostic LOGPROBS-based evaluation scheme of plausibility knowledge in LMs. In fact, most of the prompting methods lead to chance-level performance or below-chance performance for most models (Figure 5 ), even though their log probabilities evidence substantial knowledge about what events are plausible vs. implausible. This result is in line with Hu and Levy (2023)'s finding of a competence-performance gap when probing models' metalinguistic judgments.Result 2: LOGPROBS in base and instruction-tuned LMs encode substantial plausibility knowledge but fall short of human performance.The LOGPROBS results in Figure 1 show that LMs acquire substantial plausibility knowledge from distributional linguistic patterns; all of them performing well above chance on the task. Nevertheless, they also consistently fall short of human performance: On EventsAdapt (AI, impossible), all models were successful in distinguishing plausible and implausible sentences, even though all but one model (Falcon base) fell short of human accuracy of 1 (all Bonferroni-corrected ps > .05 except for Falcon base: t = -2.14, p = .02). On the more challenging EventsAdapt (AA, unlikely) subset, all models performed significantly worse than humans in distinguishing AA plausible from implausible events (human accuracy 0.95; all ps < .001). Lastly, the high task performance on DTFit shows that LMs can distinguish plausible and implausible AI event descriptions even when low-level distributional cues (like selectional preference restrictions) cannot be used to distinguish the minimal pairs. Despite this success, all models still fall short of human performance of 0.99 for this dataset at ps < .001.Result 3: Instruction tuning can worsen LOG-PROBS sensitivity to semantic plausibility.Next, we zoom in on the comparison of LOG-PROBS derived from base vs. instruction-tuned variants of the same model. Because instruction tuning constrains model behaviors to align with human-desired response characteristics (Zhang et al., 2023; Chia et al., 2023) , it is reasonable to assume that the models' learned probability distributions align better with human expectations of plausible sequences than the base variant, which might be more susceptible to the reporting bias in textual corpora (Gordon and Van Durme, 2013) . A comparative analysis of the results of base and instruction-tuned model variants across architectures reveals no beneficial effect of instructiontuning for gauging event plausibility through LOG-PROBS measurements: In all but one instance do instruction-tuned models perform similar or even slightly worse than their corresponding base model (Table 4 ). Interestingly, the gap is most noticeable for the most challenging dataset, EventsAdapt (AA, unlikely). An investigation of this difference shows that certain low-level features of the input may disproportionately affect the LOGPROBS that instruction-tuned models assign to word sequences: much of the performance difference is due to the instruction-tuned models' worse performance in discerning plausible and implausible active-voice sentences (see Figure 2 ). We quantify these effects by modeling accuracy in a generalized linear mixed-effects model (GLMM). The model uses LLM model class (Mistral, Falcon, MPT), model version (base, instruct), and voice (active, passive) as fixed effects, and items as random effects (for further GLMM model specification, see SI §D). We observed a main effect of model version (β = 0.36, p < .001) and a significant interaction between model version and active vs. passive voice (β = -0.37, p < .01). This variance highlights the fact that even though direct measurements of model-derived string LOG-PROBS in many cases encode task-relevant information (e.g., modeling of grammaticality, Warstadt et al. (2020) , of N400 effects, Michaelov and Bergen (2020), etc.), they are additionally influenced by low-level features of the input (Pedinotti et al., 2021; Kauf et al., 2023) . The kids were looking at a canary in the pet store. The bird had a little beak and a bright yellow tail.Anna was definitely a very cute child.The girl had a little beak and a bright yellow tail.The girl dressed up as a canary for Halloween.The girl had a little beak and a bright yellow tail.Table 5 : Sentence manipulations in the dataset by Jouravlev et al. (2019) . Tgt. -Target.4 Experiment 2: Context-Dependent Plausibility JudgmentsExperiment 1 has shown that LOGPROBS are a reliable, albeit imperfect, metric for probing the plausibility of isolated sentences in LMs in both base and instruction-tuned models, whereas PROMPT-ING measures are brittle and can underestimate the degree of semantic plausibility knowledge LMs encode. However, most of the time, LMs (and humans) do not process sentences in isolation, but rather as part of a larger context. In Experiment 2, we therefore compare LM judgments of semantic plausibility in short context-dependent scenarios.Given the success of LOGPROBS over PROMPT-ING in Experiment 1, we focus on comparing LOG-PROBS as measures of context-dependent sentence plausibility in base and instruction-tuned models. Specifically, we compare how the presence of (i) supporting or (ii) non-supporting but related singlesentence contexts modulates the LMs' LOGPROBS judgments. Additionally, we report results for the exact replication of the human study using Sentence Judgment prompts. 
dataset: 
 To test the sensitivity of the LM plausibility judgments to discourse context effects, we use a dataset from language neuroscience, collected by Jouravlev et al. (2019) . This dataset includes 100 items in three experimental conditions: a control condition (Control), in which the target sentence describes a plausible situation and the (optional) context sentence adds extra information; a semantically anomalous condition (SemAnom), in which the target sentence describes an implausible situation and the context sentence does not provide licensing information; and a critical condition (Critical), which shares the same target sentence with SemAnom, but here, the context sentence makes it plausible (see the examples in Table 5 ).We introduce three critical metrics to evaluate the models' context-aware plausibility judgments:General Plausibility. This metric measures the propensity of models to assign a higher probability to plausible sentences than to minimally different implausible sentence variants when no influencing context is present (similar to §3). For every dataset item, we assign a model a hit in case P (target Contr. ) > P (target Crit. ).Context-Dependent Plausibility. This metric measures the ability of models to increase the probability they assign to an a priori implausible sentence in the presence of a licensing context. For every dataset item, we assign a model a hit in case P (target Crit. |context Crit. ) > P (target Crit. ).Context Sensitivity. This metric measures the models' ability to selectively update sentence probabilities. For every dataset item, we assign a model a hit in case P (target Crit. |context Crit. ) > P (target Crit. |context Anom. ).For each metric, we evaluate model performance through the likelihood they assign either (i) a critical word within the target sentence or (ii) the target sentence as a whole. If a critical word consists of multiple tokens, we use the sum of the log likelihood scores of the word tokens. Whereas critical/target word likelihoods measure the ability of models to detect a contextually unexpected linguistic event, target sentence likelihood measures investigate whether implausibility is reliably reflected in the probability the models assign to tokens after encountering a semantically anomalous item, as well. This is because token likelihoods for plausible and implausible sentences are identical until the first contextually unlicensed word appears. Result 1: Across models, context successfully modulates the LOGPROBS of (im)plausible target words, but not (im)plausible target sentences.When comparing target word vs. target sentence LOGPROBS, a clear trend emerges: all models demonstrate consistently high performance (around 95%) across all metrics when comparing the probabilities of target words (Table 6 , Word columns); at the same time, when using the likelihoods they assign to sentences as an indicator of event plausibility knowledge, LOGPROBS plausibility judgments fail to reliably pass the sensitivity criterion. In particular, even though almost all LMs are able to distinguish plausible and implausible sentences (General Plausibility, similar to §3); and are able to modulate the probability they assign an unexpected sentence in the presence of licensing context, they fail to update the sentence probabilities selectively (this is evidenced by the substantial drop in performance for the Context Sensitivity metric across LMs). This pattern suggests that while a semantically licensing context assists the models in up-weighing the probability of an otherwise implausible target word/event description (see Context-Dependent Plausibility; in line with Michaelov et al., 2023), contextual implausibility is not reliably reflected in LMs' sentence likelihoods. In particular, once an unexpected target word has been encountered (which the LMs are able to discern, see Context Sensitivity, Word columns), the LMs appear to quickly adjust the predictions in the post-target region, in some cases assigning even higher probabilities to post-target words than in the Critical condition, with the consequence that the scores for anomalous sentences and contextuallylicensed ones differ less significantly at the sentence level. This suggests that a semanticallylicensing context helps a model in predicting an otherwise anomalous word, but the global proba-bility of the target sentence is less affected by the specific context.Result 2: Context-modulated LOGPROBS align with human contextual judgment patterns.Finally, we investigate how contextual plausibility judgments correspond to human behavior for the same stimuli. We focus on the sensibility-judgment task, in which participants were asked to decide (i) if a target sentence made sense to them within the provided context, or (ii) if it made sense to another person who did not have access to the context sentence (Jouravlev et al., 2019) . Here, we model this dataset in a 'single-participant setting', by exposing the LMs to the full items and comparing the log probabilities assigned to the target words in the three experimental conditions, with or without licensing context. Across models, we see a remarkable match between human-and model-derived plausibility scores, both in the isolated sentence and the contextualized setup (Figure 3 ; for supporting statistical analyses see SI §E, Tables 8/10 ).LOGPROBS again provide a better fit to human data than PROMPTING (Figures 3, 4 ; SI §E, Tables 8/10 vs. Tables 9/11 ), although it is interesting to observe that the prompting results for Instruct models matched the human behavioral patterns qualitatively (see also SI §F, §G). 
conclusion: 
 Overall, we show that, for both base and instruction-tuned models, LOGPROBS remain a more reliable measure of semantic plausibility than naive zero-shot PROMPTING. This is true in scenarios that evaluate both isolated and contextdependent sentence plausibility. Even though instruction-tuning has been claimed to align LMs and human brain representations (Aw et al., 2023) , other studies show that it does not always help for the alignment at the behavioral level (Kuribayashi et al., 2024) . Our results show that the base LOGPROBS estimates for simple world knowledge scenarios do not drastically change as a result of instruction tuning, showing approximately the same amount of implicitly encoded information as representation derived from next-word prediction. In some cases, however, instruction tuning can lead to less alignment of LOGPROBS to human plausibility judgments than those of base model versions.Concerning LMs' sensitivity to sentence context, we observe that by using LOGPROBS at the level of the target word, all the models perform around 90% with respect to the ground truth and are well aligned to human judgement patterns. However, when using sentence-level LOGPROBS we notice that the models have the tendency to "re-balance" the log likelihoods after processing an unexpected word, with the consequence that semantically anomalous sentences and contextually-licensed ones become harder to distinguish.Although it is possible that model-and taskspecific prompts will outperform raw LOGPROBS as a way to estimate sentence plausibility, our work highlights that LOGPROBS are an easy, zero-shot way to assess LMs' implicit knowledge. Thus, getting a raw LOGPROBS estimate of model performance can provide an initial estimate of whether or not custom prompt-based solutions can be successful or-in some cases-obviate the need for prompt tuning altogether. 
 
 Appendix: A first, obvious limitation of this work is that it has been conducted on English datasets, so we cannot be sure that our findings on LMs and event knowledge would generalize to other languages.Second, even though our prompting setup mimics that of humans, it differs in substantial ways. For example, whereas we ask LMs to evaluate sentences in isolation, participants assign scores within the context of the full experiment, having access to their answer history.Lastly, we only focused on LMs up to 7 billion parameters, due to the limit of our computational resources, and we only used three representative models in their Base and in their Instruct version. It is possible that with larger and more powerful models the performance will improve and the existing gap with human performance on distinguishing plausible vs. implausible sentences will be closed (cf. Kauf et al., 2023) .Our work aims to better understand and characterize the capacities of models, and contributes to work highlighting the importance of open access to model representations. Our work shows that LM pre-training distills a wealth of world knowledge into the models' weights, but cannot guarantee the consistency of these representations with human world knowledge. Consequently, LMs should not be expected to generate statements that are consistent with human world knowledge. General ethical concerns about LMs and their impact on human life, especially as they become more and more integrated into people's everyday lives, also apply to our work.Note that the DTFit dataset was included inHu and Levy (2023) where it was evaluated using different models and different prompts. However, they did not explicitly query the models for estimates of semantic plausibility, but rather paraphrased the LMs' pretraining task, asking which word "is most likely to come next". We include an evaluation of our models on their best-performing prompt for DTFit as a supplementary analysis (SI §B, Figure6).https://github.com/outlines-dev/outlines 


Decision: Reviewers were unanimous in their positive feedback for this work. It looks like all weaknesses and suggestions are fairly minor and not commonly agreed-upon. We look forward to seeing this paper presented at the workshop.

Reviews: review: paper_summary: paper_summary: The paper studies ways of eliciting semantic plausibility judgements from language models (base and instruction-tuned) with different methods (using model log probabilities/logprobs and with prompting) and in different conditions (with and without semantically licensing context).

The paper finds that 
- logprobs are a more reliable and consistent way to elicit semantic plausibility judgements, confirming and extending the findings of Hu and Levy (2023)
- instruction-tuning does not generally improve alignment with human judgements in these behavioral settings
- language models are able to appropriately condition on context when predicting a target word (whose interpretation should change based on the presence or absence of licensing context)
    - judgements for these words align well with human sensibility judgements when elicited with logprobs
    - results for prompting in this setting show similar trends but have some consistent issues
- while the effect of context on the prediction of a target word aligns with human judgements, this effect is obscured when the entire target sentence is presented
strength_weakness: strength_weakness: While the findings are interesting and valuable for people looking into relating language model judgements to human judgements about linguistic forms (and many of these people are part of the BlackboxNLP community), it might be of limited interest to a broader set of people. However, this is a very minor concern as it is appropriate for this workshop.
questions: questions: Since some of the questions considered are the same as Hu and Levy (2023), a more explicit discussion of how the findings in this paper relate to those findings in the introduction might help readers who aren't following this discussion very closely.
paper_summary: paper_summary: This paper compares the utility of log probabilities versus prompting for accessing LLM judgments of semantic plausibility. They compare log probabilities, zeroshot prompting judgments, and instruction-tuned prompting in two sets of experiments: the first looks at minimal pairs of sentences, the second explores the impact of context. The paper's main finding is that log probabilities remain the best way to extract judgments of semantic plausibility.
strength_weakness: strength_weakness: I would be interested in seeing whether few-shot prompting improves model alignment with human ratings, but this is more of a point for future work than a limitation of the current paper.
questions: questions: N/A
paper_summary: paper_summary: This paper contributes to a growing literature comparing raw log-probability estimates from LLMs with their meta-linguistic judgments elicited via prompting by looking at base- vs instruction-tuned models' judgments on several semantic plausibility datasets.  Looking at single sentences (in minimal pairs) in isolation, they find that log-probability performance is fairly significantly above chance and, crucially, fairly consistent across three different model types.  By contrast, the meta-linguistic judgments are much less powerful (some models are at chance) and consistent (not all models perform similarly).  A second experiment compares log-probability judgments on a dataset of sentences-in-context with several metric surrounding the probability assigned to key words or a whole sentence which has its plausibility modulated by a preceding sentence.  The results there are interesting and subtle, with a helpful comparison to human judgments as well.  Overall, the paper is very well-written and the experiments are thorough and well-done; it should be of interest to a fair chunk of the audience for this venue.
strength_weakness: strength_weakness: * Some results could be made more quantitative.
* Would have been good to see the prompting data for Experiment 2 in the main text instead of SI.
questions: questions: * Line 368 has two parentheticals in a row; I'd combine them into one and separate with a semi-colon.
* Line 401: "DTFit (AI, unlikely)".  I wasn't sure, given the earlier description of _DTFit_, whether this parenthetical is a description of the DTFit dataset, or indicates a subset of the total data (e.g. just the implausible sentences under (ii) on Line 259).  Just a small wording / clarification thing.
* Figure 2: can the role of active vs passive be made into a statistical test?  E.g. a regression of accuracy against model (possibly a random effect), base vs instruct, active vs passive, and interactions?  It seems like the claim is there's a significant interaction between instruct and active, but not necessarily other main effects.
* Line 535: two periods at the end of the sentence here.
* Section 4.4, Result 2: some sign-posting to Figure 3 would help here.  Can you also report a statistical test or correlation to quantify the "remarkable match" that is visually apparent?
* Just a comment / compliment: the results section is very well-written and something of a good model that I could imagine pointing others to as an example to follow.  I appreciate how the prose really guides the reader through the results, and relies on parentheticals to point to where in the tables/figures the evidence comes for a given claim.