xFinder: Large Language Models as Automated Evaluators for Reliable Evaluation
Authors: Qingchen Yu
Zifan Zheng
Shichao Song
Zhiyu li
Feiyu Xiong
Bo Tang
Ding Chen
Venue: ICLR 2025 Poster (2025)
Keywords: Large Language Models; Reliable Evaluation
Primary area: datasets and benchmarks

Abstract: The continuous advancement of large language models (LLMs) has brought increasing attention to the critical issue of developing fair and reliable methods for evaluating their performance. Particularly, the emergence of cheating phenomena, such as test set leakage and prompt format overfitting, poses significant challenges to the reliable evaluation of LLMs. As evaluation frameworks commonly use Regular Expression (RegEx) for answer extraction, models may adjust their responses to fit formats easily handled by RegEx. Nevertheless, the key answer extraction module based on RegEx frequently suffers from extraction errors. Furthermore, recent studies proposing fine-tuned LLMs as judge models for automated evaluation face challenges in terms of generalization ability and fairness. This paper comprehensively analyzes the entire LLM evaluation chain and demonstrates that optimizing the key answer extraction module improves extraction accuracy and enhances evaluation reliability. Our findings suggest that improving the key answer extraction module can lead to higher judgment accuracy and improved evaluation efficiency compared to the judge models. To address these issues, we propose xFinder, a novel evaluator for answer extraction and matching in LLM evaluation. As part of this process, we create a specialized dataset, the Key Answer Finder (KAF) dataset, to ensure effective model training and evaluation. Generalization tests and real-world evaluations show that the smallest xFinder model, with only 500 million parameters, achieves an average extraction accuracy of 93.42\%. In contrast, RegEx accuracy in the best evaluation framework is 74.38\%. The final judgment accuracy of xFinder reaches 97.61\%, outperforming existing evaluation frameworks and judge models.

Published as a conference paper at ICLR 2025
XFINDER: LARGE LANGUAGE MODELS AS AUTO- MATED EVALUATORS FOR RELIABLE EVALUATION
Qingchen Yu'-
Zifan Zheng Shichao Song"*
Zhiyu Li
Bo Tang' Ding Chen' Institute for Advanced Algorithms Research, Shanghai
Feiyu Xiong'
Renmin University of China
1 lizy@iaas ac. en
ABSTRACT
The continuous advancement of large language models (LLMs) has brought in- creasing attention to the critical issue of developing fair and reliable methods for evaluating their performance. Particularly, the emergence of cheating phenomena, such as test set leakage and prompt format overfitting, poses significant challenges to the reliable evaluation of LLMs. As evaluation frameworks commonly use Reg- ular Expression (RegEx) for answer extraction, models may adjust their responses to fit formats easily handled by RegEx. Nevertheless, the key answer extraction module based on RegEx frequently suffers from extraction errors. Furthermore, recent studies proposing fine-tuned LLMs as judge models for automated evalu- ation face challenges in terms of generalization ability and fairness. This paper comprehensively analyzes the entire LLM evaluation chain and demonstrates that optimizing the key answer extraction module improves extraction accuracy and enhances evaluation reliability. Our findings suggest that improving the key answer extraction module can lead to higher judgment accuracy and improved evaluation efficiency compared to the judge models. To address these issues, propose XFinder, a novel evaluator for answer extraction and matching in L.M evaluation. As part of this process, create specialized dataset, the Key Answer Finder (KAF) dataset, to ensure effective model training and evaluation. Generalization tests and real-world evaluations show that the smallest xFinder model, with only 500 million parameters, achieves an average extraction accuracy of 93.45 .2%.. In contrast, RegEx accuracy the best evaluation framework is 74.38%. The final judgment accuracy of xFinder reaches 97.61 %, outperforming existing evalua- tion frameworks and judge models. All resources for xFinder are available https://githur: com/ TAAR TAAAR-Shangee Shangha i nndrr i
INTRODUCTION
Large language models (LLMs) have seen rapid advancements in recent years (Brown al. 2020 Achiam et al. 2023). To explore the strengths, weaknesses, and differences of various LLMs,
numerous researchers have introduced a variety of evaluation benchmarks aimed at evaluating the performance of LLMs across various dimensions (Talmor et al.) 22019; Clark et al. 2018 Hendrycks ct al. 2020 Cobbe et al., (20TT) Furthermore, to facilitate comparisons of different LLMs on these benchmarks, several institutions have developed unified LLM evaluation frameworks, such LM
Eval Harness" M (Gao et al., 2 and OpenCompass (PenMMLaab) 2023). These frameworks are capable of uniformly evaluating LLM in reasoning, text generation, and domain-speciffe knowledge.
Despite the widespread application of the aforementioned evaluation benchmarks and frameworks, some researchers have raised concerns about the fairness and reliability of current LLM evaluation efforts (Aiyappa et al. 2023 Ye et al. 2024 Yu et al.) 2024). For instance, the benchmarks may
suffer from data contamination and leakage their test sets (Zhou et 2023 Oren al.) 2023)
"Equal contribution. Corresponding authors.
'LM Eval Harness is the backend for Hugging Face's Open L.M Leaderboard (Becching et al.
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Furthermore, systematic review has revealed multiple unreliabilities at various stages of the existing evaluation pipeline, as illustrated in Figure I
Question Prompting LLM Answaring
Question
Answer
fnoldd BYRAAT scong ARAVESS
Conrect
Wird
Muli choice Ouaciion
Cheke
LL Rosperea
Whates anowerd
Comeo
Wing
Transformation
Cirected
Apprepation
Key Answer Extraction Matching
Figure 1:
Typical LLM Evaluation Pipeline.
Data Item Transformation Given the uncertainty of LLM output content, evaluation frameworks often transform datasets to achieve uniform evaluation across different tasks. For example, they may convert datasets into the Multiple-Choice
Question-Answer (MCQA) with alphabet options. However, it has been shown that for some tasks, the evaluation results under this MCQA form with alphahet options may differ from those under the open-ended setting (Röttger et al. 2024).
Question Prompting and LLM Answering Various institutions may employ different configura- tions (such as temperature settings, decoding strategies, etc.) and prompts when evaluating L.LMs. LLMs are highly sensitive to these settings (Zhou et al. 2023 Zheng al. 2025). Even minor modifications (such as changing the case of letters in options or adding extra spaces) can significantly alter the model's responses (Röttger et al.) 2024 Gu et al.
2023 Sun et al. 2023 Yukun al., 2024; (Cen et al.j 2024), thus affecting the fairness of the evaluation outcomes. Moreover, while employing consistent prompts may ensure degree of fairness, uniform prompts may not suit all LLMs, potentially contradicting the initial intent of evaluating the models' inherent capabilities.
Key Answer Extraction and Matching Current evaluation frameworks predominantly rely on Regular Expression (RegEx) to extract answers from model responses. For example, they often select content following phrases like "The answer is" the key response. However, many LLMs fail to generate standardized answers (Asai et al., 2023), complicating key answer extraction from their responses. Figure fillustrates instances where current evaluation frameworks failed extract responses correctly. There are two primary scenarios where regular rules fail: first, when the model's 's response is irrelevant to the evaluation question, and second, when the response format does not conform to standards. In the first scenario, it is unreasonable to classify a model's failure to provide a relevant response an error, as the evaluation aims to measure the model's knowledge comprehension and reasoning capabilities, not merely its instruction-followt scenario, although the LLM's response effectively addresses the question, the response's non-standard format impedes correct extraction using RegEx, resulting in false judgments.
ability. In the second
Question
LLM Response
Answer
pair don't reproduce the same way? Answer choices; they have similrr way.
.. they have similar way.
Ragi affeched by Canert Vi resprris
...
(B)
(13
correct answer (F)
Which would Ad be the product of relction
balueeen theoe two alements? Answer Choices:
The anower is 19).
BegFi pwwtannniii...o
Ai Al AL AL__P___B1
The raporiie dhooses artiers and shold vebsciiaii Pio va arnwwrr
Classily the text below: Candidare category: World, Sports,
The anower World Sports.
World
M
John buys 2 pains of shoes for each of his children. They cost 560 aach. How much didl he pay?
The answer B720 Trétrffoee, the total amount John paid $360. Sorry made mistake, the arowwer should be $360.
RegEs grares logi does rot capturs comsutts))
Figure 2: Cases where LM Eval Harness and OpenCompass fail in extracting key answers. A/T/CM stands for tasks with alphabet / short text categorical label / math options, respectively.
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In summary, existing LLM evaluation practices continue face deficiencies in faimess and reliability, making reliable evaluations an urgent issue in the field of LLMs. The most effective methods for extracting and matching key answers use advanced LLMs (such GPT-4) with manual evalua- tion (Huang et al. 2024). However, due to time and financial constraints, these methods are seldom applied in practical evaluations. As result, some initiatives have developed fine-tuned judge models to automatically evaluate the LLM responses (Wang et al. 2023b] Zhu et al. (023). Unfortunately, these methods significantly lag behind advanced LLMs or manual evaluations in generalizability.
To address the conflict between faimess and cost in LLM evaluation, we propose xFinder, a novel evaluator for answer extraction and matching. xFinder achieves higher judgment accuracy than RegEx- based methods and judge models, thereby improving evaluation reliability. The core of xFinder is constructing a large-scale L.LM response evaluation dataset and developing precise, efficient model to serve as the evaluator. By replacing RegEx-based answer evaluation methods, xFinder assesses LLM responses more accurately and efficiently, further enhancing evaluation reliability.
To construct xFinder, we initially created the Key Answer Finder (KAF) Dataset. We fine-tuned 19 LLMs of varying sizes using the training set, and xFinder performed well on both the test and generalization sets. For instance, xFinder-qwen 1505 achieved an average extraction accuracy of 93.42% on the generalization set, demonstrating the model's substantial generalizability. Subse- quently, we compared the judgment accuracy of xFinder with mainstream evaluation frameworks and LLM--aased judge models (Pandal.M, Judgel.,, and GPT-4) on the generalization set. The results show that xFinder achieves a judgment accuracy of 97.61 %, significantly outperforming Pandal LM (51.9%), JudgeLM (78.13% for the 33B model), and GPT-4 (84.2%). Finally, we used xFinder and a traditional RegEx extraction module assess 10 LLMs on common of real-world tasks and ranked their performance. We found that ReglEx-basseee answer extraction components in existing frameworks often fail to extract answers correctly or fail entirely. Moreover, rankings from different evaluation frameworks exhibit sigmificant inconsistencies. In contrast, our fine-tuned xFinder models consistently produced stable L.MM rankings, demonstrating greater evaluation reliability.
We summarize our primary contributions as follows:
We provide a comprehensive review of LLM evaluation processes in the industry, identifying critical factors that can lead to unreliable evaluation results.
We introduce xFinder, a novel evaluator for answer extraction and matching in LLM evaluation, and construct the KAF dataset to support its training and testing.
Our experiments show that both RegEx-based frameworks and automated judge models exhibit unreliability, leading to low judgment accuracy. In contrast, our method achieves higher accuracy and improved evaluation efficiency.
RELATED WORK
From the perspective of LLM capabilities, current evaluations can be categorized into knowledge and capability evaluations (Lyu et al.] (024), vertical domain evaluations (Liang al.. 2024a), and alignment and safety evaluations (Li et al. 2024] Guo et al., 2023). When considering the types of questions in benchmark datasets, evaluations can be divided into objective questions and subjective questions. For objective questions, metrics like accuracy and Fl-score are widely used. These metrics rely on the ability to exactly match the LLM's response with the correct answer.
Currently, many popular unified LLM evaluation frameworks exist, including LM Eval Harness (Gao al. 2021), OpenCompass (OpenMMLab) (023). UltraEval (He al.) 2024). and OpenAI OOeenA 2024). When using objective question datasets to evaluate LLMs, these frame- works first conduct data item transformation, converting classification and fill-in-the-blank questions into multiple-choice questions. They then extract and match key answers using RegEx methods. This process involves either extracting option letters for text response evaluation or calculating the probabilities of option letters during generation for first-token evaluation. However, both methods have notable drawbacks when evaluating objective questions. In text response evaluation, inaccurately extracting the key answer from the LLM's response can lead to incorrect evaluation results. On the other hand, first-token evaluation may not accurately reflect the LLM's intended answer, as overlooks the LLM's reasoning process (Wang al. 2024c).
Evals
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In addition to RegEx-based answer extraction, recent research increasingly focuses fine-tuning LLMs for evaluation, known as judge models. Specifically, these methods include training evalu- ation models to select the better response to the same question (Pairwise Selection) (Wang et al.) 2023b Zhu et al.) 2023 Li et al. 2023), and training models to directly score a given question, response, and reference (Pointwise Grading) (Zheng et al. 2024 [i et al., [023] Kim et al. 2023). However, these judge models demonstrate poor generalization when evaluating different LLMs and benchmarks (Laskar et al.
2023 Huang al., 2024). In open-domain QA tasks, recent work has introduced the EVOUNA dataset, which can be used to evaluate the accuracy of automated evaluation
methods (Wang et al.) | (2024)).
Recently, the concept of reliable evaluation has been proposed This concept aims to rigorously assess the authenticity of content generated by LLMs and the potential risks to information systems (Augen- stein et al. 2023). However, systematic research and efforts to improve reliable evaluation are still lacking. To the best of our knowledge, our work the first to conduct a systematic study in this area.
PROBLEM DEFINITION
The existing methods described in Section aim to align the scores assigned by evaluation models with human-cxpected scores. In other words, these methods strive to ensure that the evaluation model's judgments of LLM outputs match human judgments. Since scoring is subjective, unlike the objective determination of correctness, these methods are more suitable for open-ended tasks like text translation and summarization. In contrast, our method aims to align xFinder's 's extraction of key answers from LLM outputs with the key answers expected by humans. Fundamentally, this method is more suitable for tasks with deterministic answers, such multiple-choice questions and mathematical problems. We define the key answer extraction task as follows:
Let D denote an evaluation dataset consisting of triplets: question, a set of options, and the correct answer, as shown in Equation I Here, q denotes the question, C' denotes the set of options, and denotes the correct answer. Let Σ be the set of all tokens and P the power set.
D c aa)
E Σ", C P(Σ")}
Each data point Cg a) in D is fed into the LLM,, producing an output y. shown in Equation 2 LLM(( C,
If unique substring in y be identified or Converted question wrapped- -heee this substring is considered the Key Answer Sentence s. Intuitively, is sentence that contains the final answer the question q. The definitions of these three types and their corresponding sets are as follows:
of the three types- Direct, Prompt wrapped,
Direct
This refers to s directly providing the final answer. Its corresponding set is specifically defined as shown in Equation 3 Here, T Σ + P(E*) denotes the function for synonym transfor- mation, such as 7 (Option A) {A, The First Option, (corresponding content of the option). "....
Prompt wrapped Here, consists of relevant prompts combined with the final answer, as detailed in Equation The subset F c P(E*) represents collection of prompts guiding the answer, where the placeholder <final answer> indicates the position of the final answer. The expression s o denotes the new sentence formed by replacing the <tinal answer> token in f with
Converted question wrapped In this approach, 8 consists of the original question q converted into declarative statement, combined with the final answer. The corresponding set is specified in Equation 5 The function Ç X + Σx represents the statement conversion function, which transforms q into its declarative form and identifies the position of the placeholder. For example, <(What is the age of Mark 10 years later?) Mark will be <final answer> years old in 10 years.
Particularly, when multiple substrings meet the criteria, Thought (CoT) process" exists within y. If a CoT process is present, s is defined as the unique
necessary determine if Chain of
https::
/topss:/att / tes. google www.cooovvioo.ooo..ooo vwwe/veeeeeee i fo2024 /012/v/e
CoT (Chain of Thought /Wei et al. 2022 refers to the process of progressively reasoning through arrive at the final result.
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substring that meets the criteria and appears after the CoT process in y. If no CoT process is present, s does not exist in y.
S (c,)(c) CEC}
S₂ {so 8 S SI.fFF)
S3 {8 S (())
(5)
The definition of the key answer & is divided into scenarios:
1. If s does not exist, k is set to [No valid answer];
2. If s exists, is the option within the set that best corresponds to what is described by s, defined in Equation 6
3 s.t.aa (k)
(6)
METHODOLOGY
To fine-tune xFinder, high-quality dataset is essential. To our knowledge, there is no dedicated dataset for key answer extraction and matching. this section, we will detail the implementation of xFinder, including the KAF dataset construction and model training, illustrated in Figure 3 The KAF dataset encompasses variety of evaluation tasks, including questions, optional answer ranges, LLM responses to the questions, and the extracted key answers. The dataset is divided into three segments: training, test, and generalization sets, used for fine-tuning, testing, and performance assessment, respectively. The training has 26,900 samples, the generalization set 4,482 samples. For more detailed information, refer to Appendices B.1 and B.3 Section 4.1 details LLM responses collection, Section 4.2 describes the annotation of key answers in LLM-ggnnrated content, and Section 4.3 elaborates on our detailed approach to training xFinder.
4,961 samples, and the
Responce
da MMLU
3| CSOA
I ARC
+ OpenBBokk
Likea
poep preiee tan wrllie luter the company
This soerr dhoudd choose eprionn (i) vel the taacher oborr bodla focud ia Important
coavex
MAlk
Operil
LLM Response
LLMs Engine
Pair Format
GPS-
CPT-
Finol Metric
Exskaation
Unroliable
Evalustion
Kay Extracting Candidute One
Kay Extracting Candkdute Two
B0.aKe
71.0%
23%
XFinder
Matching
Consistency Strategy
OPT-4
Response
Answer
xFiinder
Response
Answer
Candidate Answer
KAF Basic
Human Recheok
Question
Response
Answer
Final Answer
Pipeline
Dataset Finder Training
Aude Labeling
Figure 3: Schematic of the research framework. The first three stages correspond to Sections 4.1 4.2 and 4.3 while the final stage illustrates the replacement of RegEx with xFinder in the evaluation pipeline. The experiments in Section 5.3 demonstrate the efficacy of our approach within this pipeline. Note: The percentages 80.3%. 77.0%. and 2.2% in the center of the figure around "unreliable evaluation" indicate results from the Hlama3-B-IInstuue on the GSM8K benchmark using RegEx evaluation via the LM Eval Harness, OpenCompass, and UltraEval frameworks, respectively, while our method achieves reliable result of 80.2%.
4.1 LLM RESPONSE GENERATION
As shown in the first part of Figure initially generate question-response pairs using different LLMs across multiple datasets. Currently, the tasks for evaluating LLM performance are highly di- verse. We investigated the leading L.M benchmarks and evaluation reports cover popular evaluation
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tasks. Ultimately, selected 19 typical evaluation task datasets, with detailed information provided in Appendix [A.]] We categorized these tasks into four types, each with similar option formats serving prompts to induce LLMs to generate varied responses (as shown in the left boxes in Figure 2 (1) Alphabet option tasks. By referring to the design of mainstream evaluation rankings, these tasks are formatted as alphabet options, where each task aims to select the single correct option from multiple possibilities (i.e., A, B, C, D, etc.). (2) Short text option tasks. The requirement for the model to map letters to option texts may impact the evaluation of the model's true capabilities (Wang al. 2024b). Therefore, we designed tasks with key answer types short texts, where the goal is to select the correct option from multiple short texts. (3) Categorical label tasks. For traditional classification tasks (e.g., sentiment classification, topic classification, entity classification), we retained their original format, using the labels themselves as options. (4) Math option tasks. Given the variety of output types in math tasks, such as integers, decimals, and LaTeX code, we categorized them separately.
To enhance the diversity of the dataset, we designed eight types of prompt configurations to elicit varied responses from the LLMs. These configurations include O-shot(-restric 0-shot-cot(-restriic)) 5-shot(-restriet), and 5-shot-cot(rrestrict). These prompt templates are provided in Appendix M
For generating responses, it is crucial to capture wide range of output content, given that LLMs of different series and sizes exhibit varied reasoning processes and may provide different answers to the same guestion. Consequently, about these models can be found in Appendix A.2 The KAF dataset's s training and test sets include question-respone pairs generated by 10 LLMs across evaluation tasks. To ensure the validity and faimesss of the generalization evaluation, the question-response pairs used in the generalization set are derived from responses by LLMs on 11 entirely new evaluation tasks. Further details on the KAF dataset can be found in Appendix B.I
utilized a total of more than 10 distinct LLMs, and specific details
4.2 AUTO LABELLING AND HUMAN RECHECK
The foundation of the KAF dataset is extracting the key answer & from the LLM response y. which serves as the label. We employed distinct annotation strategies for the training, test, and generalization sets. For the training set, to improve annotation efficiency, we adopted a semi- automated procedure. This involves generating two sets of annotations from GPT-4 with different prompts (refer to Appendix| F22 for prompt templates). Using the self-consistency strategy (Wang) ct al. 2023a| Liang et al., 2024b). items with discrepancies between the two annotation rounds, along with all mathematics questions, were manually re-annotated using Label Studio (Tkachenko et al.) 2020). Two rounds of manual labeling were conducted to ensure the accuracy and reliability of the test and generalization sets. Detailed information about the annotation methods can be found in Appendix B.2
4.3 TRAINING XFINDER
According to the definition of key answer extraction outlined in Section 3 our goal is to fine-tune xFinder to precisely locate within y and accurately extract l from 8. The training input for xFinder includes the question q, the LLM response y, the key answer type, and the answer range, while the output the key answer k.
During the training process, we primarily utilized the XTuner (InterLL 2023) tool developed by the InternLM team for fine-tuning. To fully exploit the instructiion-lllooinng potential of xFinder, we meticulously crafted prompts as demonstrated in Appendix F.4, Additionally, to enhance the generalization capability of xFinder, we implemented data augmentation on the KAF training set, which included:
Simulating LLM responses to alphabet option questions. We randomly selected 50% of the alphabet option questions from the KAF training set and altered their answer choices by either adding one or two options or removing one option with equal probability.
Enriching the variety of prompt forms in Prompt wrapped key answer sentences. From the L.M responses in the KAF training set that contained wrapped key answer sentences, we
rrestrict. indicates that the prompt ends with the sentence "End your final answer with "The answer is <answer>." standardize the I.LM's response format.

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extracted 10% and substituted the f with other elements from F, such replacing "The final answer A" with "Based on the context of the question, A is the most likely answer."
EXPERIMENTS
In this section, we first outline the experimental setup, including metrics, baselines, and fine-tuning configurations, which apply to the Extraction Accuracy and Judgment Accuracy experiments.
Metrics, For the Extraction Accuracy and Judgment Accuracy experiments, we employ two primary metries: Extraction Accuracy, measuring the proportion of correctly extracted key answers, and Judgment Accuracy, evaluating the correctness of judgments on LLM responses. Detailed definitions of these metrics provided in Appendix [.1]]
Baselines. The baselines for these two experiments include mainstream LLM evaluation frameworks such as OpenCompass (OpenMMLaa) 2023), LM Eval Harness (Gao al.) [021), and UltraEval (He et al. 2024), all of which rely on RegEx-based extraction methods. Additionally, we compare with judge models such as PandaLM (Wang et al., (023b) and JudgeLM (Zhu et al. 2023), well as GPT-4, which serves as both extractor and judge (the prompt used is provided in Appendix
F22 and Appendix F3)
Fine-tuning Details. For the experiments, we fine-tuned 19 LLMs, with parameter sizes ranging from 0.5B to 8B, using the KAF training set. Fine-tuning was performed using the QLoRA method from the XTuner framework (InterL.M 2023 Dettmers et al., 2024). with detailed parameters provided in Appendix C
Finally, for the world Evaluation experiment, we evaluated the performance of 10 LLMs on 14 evaluation task datasets, comparing results obtained using xFinder-gwen 155,, the mainstream evaluation frameworks. This experiment analyzes differences evaluation outcomes establish xFinder's reliability in practical applications.
and
5.1
EXTRACTION ACCURACY: XFINDER REGEX
Results on KAF test set We employed the QLoRA method from the XTuner framework (InterLM 2023 Dettmers al. 2024) for fine-tuning the foundation models, with specific tuning parameters detailed in Appendix To evaluate the effectiveness of xFinder, we conducted tests using the KAF test set. We the mainstream frameworks OpenCompass, LM Eval Harness, and UltraEval with their RegEX extraction modules baselines. The comparison metric is extraction accuracy, defined as the proportion of correctly extracted key answers relative to the dataset size. Partial results are presented in Table For complete experimental results, refer to Table 22 in Appendix D.2
Table 1: Comparison of Extraction Accuracy on KAF Test Set: Mainstream frameworks vs, xFinder models. Specifically, xFinder-qwen 1505 and XFinder-llama38it represent models fine-tuned with the KAF training set on Qwen 1. .5-0.5B and Llama3-8B-Instrucc, respectively.
Method
alphabet option short text categorical label
math
Overall
OpenCompass
0.8098
0.7481
0.7937
LM Eval Harness
0.8500
0.7540
0.8208
0.7496
0.8113
Ultralival
0.6057
/
0.4766
0.5722
XFinder-qwenl505
0.9735
0,9683
0.9805
0.9276
0.9688
XFinder-qqenl5118
0.9735
0,9781
0,9755
0.9457
0.9712
xFinder-gemma7
0.9751
0,9805
0.9736
0.9367
0.9704
<Finder-chatgll36bass
0.9735
0.9793
0,9730
0.9291
0.9684
XFinder-Ilama38
0.9698
0.9829
0.9692
0.9276
0.9661
It is evident that the xFinder models, fine-tuned using the KAF training set, highly effecttve. Furthermore, despite a 16-fold difference in the parameter sizes of the models, the variation in extraction accuracy does not exceed 0.5%.
Results on KAF generalization set To further explore xFinder's 's extraction capabilities on not-seen tasks, conducted tests using the KAF generalization Specific results are shown in Table 2
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Only two xFinder models displayed here for brevity, but the complete experimental results be found Table [3 of Appendix [D.2] The results indicate that xFinder maintains high level of extraction accuracy on the generalization set. In contrast, mainstream RegEx methods perform poorly 5 especially in extracting key answers for mathematical problems. Even GPT-4 struggles to extract key answers from complex and varied LLM responses accurately.
Table 2: Comparison of Extraction Accuracy KAF Generalization Set: Mainstream frameworks,
GPT-4, and xFinder models. Specifically, xFinder-qwent 505 and xFinder-llamaã8ii represent models fine-tuned with the KAF training on Qwen1.5-0.5B and Llama3-8biinstricc, respectively.
Method
alphabet option
short text categorical label math
Overall
OpenCompass
0.7750
0.6813
0.7438
LM Eval Harness
0.6594
0.7484
0.8381
0.2094
0.6780
UltraEval
0.5945
/
0.1781
0.3978
GPT-4 as Extractor
0.6378
0.8046
0.6706
0.6703
0.6957
xFinder-qwen1500 1505
0.9477
0.9335
0.9281
0.9234
0.9342
0.0277
0.0554
xFinder-Ilama3ii
0.9547
0.9428
0.9537
0.9547
0.9518
0.0453
0.0057
5.2
JUDGMENT ACCURACY: XFINDER VS. JUDGE MODELS
As shown Table we present the evaluation results of mainstream frameworks, judge models, GPT-4, and xFinder on the generalization set. The metric is Judgment Accuracy, which measures the correctness of judgments on LLM responses. observed that the judgment accuracy PandaLM was only 51.9%, while Ju3geLM-33BB achieved merely 78. 13%. In addition, the highest accuracy of GPT-4's judgments is only 88.42%, which is significantly lower than that of xFinder.
Table 3: Comparison of Judgment Accuracy on KAF Generalization Set: Mainstream frameworks, judge models, GPT-4, and xFinder, using human-annotated results as ground truth. All judge models use prompts with reference answers. Method
alphabet option
short text
categorical label
math
Overall
OpenCompass
0,8742
0.9125
0,8870
LM Eval Harness
0.8117
0.9148
0.9750
0.5813
0.8592
UltraEval
0.7836
/
0.5328
0.7000
PandaL
0.4953
0,5832
0.5312
0.4391
0.5190
JudgeLM-7BB
0.7195
0,8316
0.8056
0.5875
0.7555
JudgeLM-133B
0.6875
0.8545
0.7694
0.9266
0.7867
JudgeLMM33B
0.8133
0.8358
0.6906
0.8625
0.7813
GPT-4 as Judge
0.9016
0.8909
0.7294
0.9313
0.8420
GPT-4 Judge (CoT)
0.9234
0.9345
0.7919
0.9609
0.8842
xFinder-qwenI500
0.9781
0.9761
0.9625
0.9969
0.9748
(Finder-llmma3it
0.9750
0.9688
0.9731
0.9969
0.9761
We present the differences between answer Extraction Accuracy and final Judgment Accuracy in Table 4 From the results, we can see that the baseline method with the smallest difference, OpenCompass, still has judgment accuracy 14.32% higher than its extraction accuracy, indicating a high unreliability in the evaluation results obtained using traditional RegEx-based based key answer extraction methods. In contrast, the judgment accuracy of our 2.43% higher than its extraction accuracy, reducing judging reliability due to inaccurate extraction to within 3%.
method is only
Efficiency Comparison To further compare our xFinder with existing LLM as judge models, including JudgeLM, PandaLM, and the API-based GPT-4, we conducted cost analysis experiment. We randomly sampled 200 instances from four question types within the Generalization Set. As we present the time required evaluate these tasks on the same machine equipped
shown Table 5
Here, the extraction accuracy of OpenCompass, LM Eval Hamess, and UltræEval has significantly decreased compared to Table [ This is because the LLM responses in the KAF test set are generated from restricted prompts, making it casier for RegEx methods to extract the key answer; However, the responses in the KAF generalization generated from non-restricted prompts.
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Table The difference, that is Accuracy Gap (4), between Judgment Accuracy (1) and Extraction Accuracy (1) on the Generalization Set for Mainstream frameworks, GPT-4, and xFinder. It reflects discrepancies in Judgment Accuracy due to extraction errors. This set uses auman--nnotated results as the ground truth.
Method
Extraction Accuracy
Judgment Accuracy 1
Accuracy Gap 4
OpenCompass
0.7438
0.8870
0.1432
LM Eval Harness
0.6780
0.8592
0.1812
UltraEval
0.3978
0.7000
0,3022
GPT-4
0.6957
0.8420
0,1463
xFinder-qwen 1505
0,9342
0,9748
0,0406
4Finder-llama33e
0.9518
0.9761
0.0243
with 8 NVIDIA H100 (80G) GPUs. Notably, JudgeI.M-339 utilized 2 GPUs, while the other models operated on a single GPU, with all evaluations conducted in a single process. Table [ we present the complete evaluation tasks along with the costs incurred by the API-based GPT-4. The cost of xFinder-qwen1500 under the aforementioned setup is merely S0.02, accounting for only 0.4% of GPT-4's expenses. It can be observed that xFinder achieves not only higher accuracy compared to these judge models but also lower evaluation costs and greater flexibility.
Table Time costs (in seconds) for xFinder and judge models in evaluating 200 randomly selected questions per task type from KAF generalization set.
Methods
alphabet option (s)
short text (s)
categorical label (s)
math (s)
Avg Time
PandaLM-7B
71.05
70.09
56.45
38.88
59.12
JudgeLM--B
228.11
227.83
240.54
330.40
256.72
JudgeLM-133
395.57
457.49
415.46
415.31
420.96
J-333I
$22.05
527.63
517.25
571.82
534.69
4Finder-qwen1500 505
10.24
11.12
10.05
11.28
10.67
xFinder-llama38it
13.43
16.79
12.79
16.80
14.95
Table 6: Total costs (in USD) for GPT-4 API in evaluating 200 randomly selected questions per task type from KAF generalization Methods
alphabel option ($)
short text (S)
categorical label (S)
math ($)
Overall ($)
GPT-4 as Extractor
1.34
1.2
1.13
1.39
5,06
GPT-4 as Judge
1.25
1.14
1.19
1.57
5.15
5.3
EVALUATION IN REAL- SCENARIOS
The experiments conclusively demonstrate that xFinder plays critical role within evaluation frame- works. Next, will use xFinder for automated evaluations in real-world evaluation tasks instead of only inspecting the extraction accuracy. To balance efficiency and quality, we calculated two metrics for all xFinder models the KAF generalization set: Aace and A (where N is the parameter size of the L.M,, measured in billions). Based on these metrics, xFinder-qwen 1505 505 (with the highest Aoce/M /N) and xFinder- (with the highest Aace) were selected. Here, Bace represents the difference in extraction accuracy on the KAF generalization set between the current xFinder and the lowest accuracy among the 19 xFinder models, For specific results, refer Table 23 Appendix [..
We evaluated the performance of 10 LLMs across 14 mainstream evaluation tasks using xFinder- qwen 555 and Haress,, and UltraEval. The comprehensive scoring results are provided in Appendix D.3
along with RegEx methods based on OpenCompass, LM Eval
Setup Currently, the parameter settings for each evaluation framework differ significantly, including preprocessing methods for task datasets, prompt template configurations, and hyperparameter settings for the models being tested. Therefore, to compare the performance of our xFinder extraction method with RegEx methods these frameworks, framework and ensured consistency in other settings. This primarily included: (1) applying uniform preprocessing the task datasets; (2) employing consistent zero-shot approach for all evaluation
replicated only the RegEx methods on each

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Published as a conference paper at ICLR 2025
tasks, with specific prompts detailed in Appendix F.1 and (3) selecting the following LLMs for comprehensive evaluation: Phi-2, Baichuan series, Gemma series, Llama series, IntemLM series, and Qwen series. Additionally, ensure the stability of experimental results and maintain consistency in parameter settings across different LLMs, we set the temperature to 0, top_p to 0.9, and top_k kk to
Commensemue@A inlphbbet
Commonsemsoo Prett)
Lev0:
IRtalleees
Reviraa
BedQ
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shaeeeae sharcvnns Intaiiaru
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Figure 4: Bump charts: Changes in LLM rank over different evaluation frameworks.
Results and Analysis Figure Segure/jillsseee iillustrates the ranking changes of LLMs across four tasks based on evaluation results extracted from different frameworks. Additional chart results can be found in Appendix E
From the analysis, we can derive the following insights:
Are existing evaluation frameworks reliable? The graph reveals significant discrepancies the rankings of LLMS obtained using different evaluation frameworks. For instance, in the Common- senseQA (alphabet) task, InternI.M2-1. --t-Chttt ranks first in UltraEval but only fifth in OpenCompass. Such disparities in rankings indicate the unreliability of RegEx methods within mainstream evaluation frameworks, potentially affecting the credibility and ranking of model evaluation.
RQ1:
RQ2: Is xFinder reliable? Firstly, by comparing presented checkpoints, xFinderrqwen 1505 and Ilaaaest we observe relatively consistent rankings of LLMs across different base models on the same task. This demonstrates the high stability of xFinder, even Commonsense datasets that have not been used for training. Furthermore, considering the extraction accuracy of key answers from previous experiments, xFinder exhibits higher accuracy compared to the RegEx methods in other frameworks. This implies that our evaluation results are both stable and accurate. Therefore, can be concluded that our model demonstrates higher reliability compared to other frameworks.
Is setting evaluation tasks as alphabet options reliable? Existing evaluation frame- works often aim to convert all questions into multiple-choice format with alphabet options. However, as shown in Figure 4. there are significant differences in the rankings of the CommonsenseQA task when using the alphabet option format compared to the short text format (more experimental details can be found in Appendix E.I). This suggests that alphabet options may be unreliable, and we advocate for reducing the reliance on the alphabet option format in existing evaluation frameworks.
(additional) RQ3:
CONCLUSION
In this paper, we analyzed key issues in existing LLM evaluation frameworks that could result in unreliable outcomes, with a particular focus on the extraction and matching L.M responses. In response to these challenges, we introduced xFinder, a novel evaluator specifically designed for accurate key answer extraction and matching in LLM responses. Extensive experiments showed that xFinder extracted key answers from LLM outputs more accurately and efficiently than existing RegEx-based methods, significantly improving evaluation reliability. This study represents the first step in constructing reliable evaluation framework. In the future, will have continued addressing other key issues in the reliable evaluation of LLMs, providing a foundation for accurately evaluating I.LM performance.
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Appendices
A
Datasets and Models
15
Dataset Details
15
A.2 Large Language Models
15
KAF Datasef
16
B.
1
Dataset Structure
16
B.2
Human Annotation Details
19
B.3 Examples from the KAF Dataset
21
Fine- Fine-Tuning Hyperparameters
22
D Detailed Experimental Results
23
D.I Example of Extraction vs. Judgment Accuracy
23
D.2 Extraction Accuracy Results
................................
24
D.3 Evaluation in Real-Worke Scenarios Results
25
Bump Charts of All Tasks
30
E.
Alphabet & Short Text Option Tasks
30
................................
Categorical Label Option Tasks
................................
33
E.3
Math Option Tasks
34
Prompts
35
Prompts for LLM Responses Generation & Real-World Scenarios Evaluation
35
F2
Prompts for Auto Labeling Using GPT-4
40
F3
Prompts for Judge Models Using GPT-4
42
Prompts for Extracting Key Answers Using xFinder
43
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A DATASETS AND MODELS
A.1 DATASET DETAILS
Table provides summary of all the datasets utilized in our experiments. We undertook extensive preprocessing activities on raw data from these datasets, ensuring that all samples were trans- formed into a consistent and unified format. From our original dataset, we sampled 2000 data points from the training set and 1000 data points from the test set (or dev set a test set was unavailable). Subsequently, categorized the questions into four types based the key answer format in the dataset: alphabet, short text, categorical label, and math type.
Table 7: Datasets Description. The datasets sorted by their names in alphabetical order. Key Answer Type refers to the format the key answer: A/T/C/M stands for alphabet short text / categorical label math type options respectively. Asterisk (*) denotes that the data within these datasets are utilized for fine-tuning our xFinder model.
Dataset
Key Answer Type
#Train
#Test
License
AgNews
2000
1000
Unspecified
Amazon
C
2000
1000
Apache-2.0
ARC-challenge"
A/T
2000
1000
CC BY-SA 4.0
ARC-easy"
A/T
1391
1000
CC BY-SA 4.0
BOOIQ
1000
CC BC-SA 3.0
ComonsenseQA
A/T
2000
1000
MIT License
DBPedia
C
2000
1000
CC-B ---------00
GSM8K
M
2000
1000
MIT License
hellaswag
2000
1000
MIT License
MATH
1974
986
MIT License
MetaMathQA
M
2000
1000
Apache-2.0
MMLU
A/T
2000
638
MIT License
MultiArith
M
420
180
Unspecified
OpenbookQA
A/T
2000
500
Apache-2.0
QNLI
2000
1000
Unspecified
SIQA
A/T
2000
1000
Unspecified
Subj
C
2000
1000
Unspecified
TREC
1000
500
Unspecified
WIC
2000
CC BY-NC 4.0
A.2 LARGE LANGUAGE MODELS
We selected the models listed in Table from a vast array of open-source LLMs for our experiments.
Table 8: LLMs sorted by release date. All LLMs chat instruct models. Asterisk (*) denotes
estimated value. NaN denotes no public data available, and 175B denotes 175 billion.
Model
#aara.
Type
Publisher
Date
LLaMA2
7B
Chat
Meta
2023.07
Baichuan2
7B
Chat
Baichuan Inc.
2023.09
ChatGLM3
6B
Chat
Tsinghua
2023.10
GPT4-1106-previeww
NaN
Chat
OpenAl
2023.11
Phi-2
2.7B
Base
Microsoft
2023.12
InterL.M2
7B
Chat
ShLab
2024.01
InternI
1.8B
Chat
ShLab
2024.01
Qwen1.5
14B
Chat
Alibaba
2024.02
Qwenl.5
4B
Chat
Alibaba
2024.02
Qwenl.5
1.8B
Chat
Alibaba
2024.02
Qwenl.5
0.5B
Chat
Alibaba
2024.02
Gemma
7B
Instruct
Google
2024.02
Gemma
2B
Instruct
Google
2024.02
Qwen1.50 1.5
14.3B
Chat
Alibaba
2024.03
LLaMA3
8B
Instruct
Meta
2024.04
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B KAF DATASET
B.1 DATASET STRUCTURE
The KAF dataset is designed to enhance the accuracy and reliability of key answer extraction in LLMs. It is divided into three segments: training set, test set, and generalization set, each serving distinct purposes in the development and evaluation of the xFinder model.
B.1.1 TRAINING SET
The training contains 26,900 samples. Detailed information about the models, key answer types. and prompt configurations used in the training set is presented in Tables 10 respectively.
and 12
Table Number of samples from each LLM in the training set. Asterisk (*) denotes our manually expanded enhanced data.
LLMS
Count
Baichuan2-P7 7B-Chat
3726
Qweal.5 1--5--4--hh-
3051
Anonymity"
2972
Qwen 1ren1.5-1.8B-Cher
2724
Gemma-7B4
2409
MMtemL.M-7BB-0
2383
Gemma-2B-
2214
LLaMA2-7B-Chen
2102
Qwenl.5-0.5BB --O.SBhhhh
1927
ChatGLM3-6B
1755
PHI2
1637
Table 10: Number of samples for each dataset in the training set. "enh" indicates the enhanced alphabet option questions.
Dataset
Count
AgNews
1813
Amazon
2029
ARC-c (alpha)
2452
ARC-c (enh)
577
ARC-c (text)
2549
ARC- (alpha)
2403
ARC-e (enh)
581
ARC-e (text)
2670
DBPedia
2303
GSM8K
3394
MATH
1540
MMLU (alpha)
2439
MMLU (enh)
574
MMLU (text)
1576
Table 11: Number of samples for each key answer type in the training set.
Key Answer Type alphabet option short text
Count
9026
6795
categorical label math
6145
4934
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Table 12: Number of samples for each prompt configuration in the training set.
Prompt Configuration
Count
random-O-shot
3341
-andom---shot-cot
5201
2976
mandom----hhot-esttrie
2350
random-S-shot
3267
random-S-shot-cot
5321
random-5-shin -eaess-sottttt-rssttieee
2390
random-5 -aim-----st--rtrriii
2054
B.1.2 TEST SET
The test set comprises 4,961 samples. Detailed information about the models, key answer types, and
prompt configurations used in the test set is presented in Tables 13
15 14
and 16. respectively.
Table 13: Number of samples from each LLM in the test set.
LLMs
Count
Baichuan2 2-7-Chhtt
786
Qwen1 1.-e1 1-4B-Chat
669
Qwenl.5-1.8B-CH.
545
I2ternL.M--BB-hhaa
541
Gemma-7B-it
495
LLaMA2-7B- Chatht
436
Phi-2
412
Gemma-2B 2B-it
398
MM-GGMM3-6B
344
Qwen 5--055h-hhat
335
Table 14: Number of samples from each dataset in the test set.
Dataset
Count
AgNews
478
Amazon
600
ARC-c (alpha)
651
ARC-c (text)
345
ARC-e (alpha)
584
ARC-e (text)
387
DBPedia
512
GSM8K
663
MMLU (alpha)
MMLU (text)
89
Table 15: Number of samples for each key answer type in the test set.
Key Answer Type alphabet option short text categorical label math
Count
1887
821
1590
663
Table 16: Number of samples for each prompt configuration in the test set.
Prompt Configuration
Count
random-O-sho
1157
random-O-shot-cot
1248
random-S-shot
1270
random-S-shot-cot
1286
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Published as a conference paper at ICLR 2025
B.1.3 GENERALIZATION SET
The generalization set comprises 4,482 samples. Detailed information about the models, key answer types, and prompt configurations used in the generalization set is presented in Tables 17 and 20 respectively.
18
Table 17: Number of samples from each LLM in the generalization set.
LLMs
Count
LLaMA2-7B-CHE
562
IntemLM2-11 --tth-hhha
562
Gemma 2B-it
561
Qwen 5-14BB-hh
560
-lamaa--B-Instruct
560
Qwen .55..o--2..7------
560
ChatGLM3-6B
559
Phi-2
558
Table 18: Number of samples from each dataset in the generalization set.
Dataset
Count
BoolQ
320
CommonsenseQQA (alpha)
320
Commonsense(AA (text)
322
hellaswag (alpha)
320
MetaMathQA
319
MultiArith
321
Openbook QA (alpha)
320
OpenbookQA (text)
320
QNLI
320
SIQA (alpha)
320
SIQA (text)
320
Subj
320
TREC
320
WiC
320
Table 19: Number of samples for each key answer type in the generalization set. Key Answer Type
Count
alphabet option short text
962
categorical label math
1600
640
Table 20: Number of samples for each prompt configuration in the generalization set.
Prompt Contiguration
Count
random-O-shot
1122
mandom-O-shot-co
1120
random-5-shi
1119
mundom-S-shot-ce
1121
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Published as a conference paper at ICLR 2025
B.2 HUMAN ANNOTATION DETAILS
In our manual annotation project, all annotators are volunteers with a background in natural language processing, and each annotator holds least a master's degree. The project team consists of five annotators, with gender ratio of three men to two women. Regarding their remuneration, they first receive the standard employee salary. We also pay them an additional RMB per annotated data item, engineering
with the average time for annotating each task being about 20 seconds. Additionally, team participates in data review, primarily responsible for supervising and evaluating annotation qualityEvery data item is annotated by two different annotators. We will recheck the annotation and make the final decision if two annotators have different results towards the same item The entire annotation process lasted for 15 days. Below are the annotation guidelines.
B.2.1 HUMAN ANNOTATION GUIDELINES
The annotation interface is shown in Figure 5 Each sample consists the following elements: Ques- tion (the question), LLM Output (the response provided by LLM for this question), Key answer type (the type answer for this question, which can be one a poghanmmeric_mmeriien / tt___eet__ee______ ext__content__t cenrt//llasee classi tassitt_text / numerical) and Answer range (the set of candidate answers for this question).
##Question
barget
aought
freed the bendagee
#FLLM Output
Filie
target ward "bondage' does have the exact same meaning lngin the centences. Sentence "e soug release from tie bondlage to Satan' the semn "sondagee" relers a mnse of captiity or enaliiement Saran, typicelly underiood a asiitual menral conatraint.
libersted comataintt being bound by the paraage of captivity imposed
augpesting g that
deKay answer type
#dAnswerr range
TTrre "Fale
Type entracted snewer hers.
tharai any special cases, pleaze hare.
Figure 5: Label Studio Interface.
Your Task. You are required fill in the following two blanks.
The first blank is for the answer extracted from the LLM Output. For non-mathematical questions, the extracted answer MUST exactly match one of the candidate answers in the Answer range. For mathematical questions, directly extract the final answer from the LLM Output, preserving the original format (including any LaTeX formatting). When no valid answer can be extracted from the LLM Output (e.g., if the answer is irrelevant or inconsistent), enter [No va id answer] (without quotation marks).
The second blank is generally left empty, but can be used for notes in special cases. If you encounter a particularly unusual LLM Output, you may leave a comment here. In other cases, leave this field blank.
Annotation Details.
For alphanumeric option questions (a ce_lphanumerii_nmmrrro..oom
The LLM Output may provide both the option letter and the corresponding option content, but you should only extract the option letter, i... "A/B/C/D" (note that the letter should be uppercase, with additional spaces, and without quotation marks).
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If the LLM Output provides both the option letter and content, but they do not match, enter [No valid answer] (without quotation marks).
For short text questions (short_texttconteee)
If the LLM Output correctly provides one of the candidate short texts from the Answer range as the answer, label it with that short text.
Note that some candidate short texts the Answer range may include punctuation (e.g., ".), which must be copied and pasted exactly as the final label, without quotation marks.
For text classification questions (classify_text)
Similarly, only if the LLM Output clearly provides a candidate answer from the Answer range should you label it with that answer, otherwise, label it [No valid answer] (without quotation marks).
If the answer the LLM Output has some kind of relationship with a candidate answer in the Answer range (e.g., inclusion, intersection), but does not explicitly indicate that candidate answer, label it as [No valid answer]. For example, if the LLM Outpur is "The answer is River." and the Answer range is ["Nature", "Technology"I it should be labeled as [No valid answer].
For mathematical questions (numerical):
If the LLM Outpur does not explicitly provide answer to the corresponding question, label it as [No valid answer).
If the LLM Output initially gives an answer ansl, but through detailed calculations, rejects a and provides final answer ans2, extract ans2 the final answer.
The label should not include commas or non-LaTeX symbols (such as "$"). For instance, if the LLM Output is "The answer $10,000.", the label should be "10000" (without quotation marks). But, the "S" symbol with the LaTex equation should be retained.
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B.3 EXAMPLES FROM THE KAF DATASET
"Key_answer_type" palphabet option", "question" "A is seen playing guitar on stage with others playing instruments pehind him. The man grabs a guitar from the aucience and begins playing both one after the other ...",
"iim outputu "Option A is the correct choice as it describes ...", "etandärti_answerreengee "[['A', 'strums the guitar in the end, continues playing the guitar with the crowd following him as well as lining up next to him.'l, ['B', continnuss playing the instruments and ends by waving to the crowd and walking off stage.'), ['c', tthen turns to the audience and gives stuffed toy to the audience and continues playing.'), ['D', 'finally stops playing and his hands for the crowd to see.']]", "gold_label", "A", "xF "xFinder_outputt.; "A",
"key_answer_typer" "short text",
"question": "If you really wanted grape, where would you go to get it? Answer Choices: winery / fruit stand field kitchen / food", "1lm output"; "The answer is winery / fruit stand / field / kitchen food ...".
"[\"winery\", \"fruit stand)", \"fieldV", \" kitchen\", \"food\"]",
aaandardd_aswer_raane
"gold_label"; "[No valid answer]", "xFinder_output"" "[N valid answer)",
,
wwwpaanswe__ppeeee "categorical label", "questicn"; "How tall is the Sears Building?",
"Lim_output"; "The Sears Building is specitic structure, so the answer would be a licattin ...", @standard_ansver_rame
"[' "['Abbreviation", 'Entity', DDeccription' Person', 'Location', "Number']", "gold label": "Location", StFinder_outpute "Location",
"key_ aeoyer_wwwr__ee "math",
"questicn"; "Mike made 69 dellars mowing lawns over the summer. spent 24 dollars buying new mower blades, how many dollar ganes could he buy with the money he had left?", "11m "!im_cutput.; "To find out hew many 5 dollar
Let's calculate that AA / $5 9'n\nSo, Mike could buy 9 dollar games with the money he had left.",
"standard_anse" answer range": "a(n) number / set / vector matrix / interval / expression function / equation / inequality", "golá_label"; "9", "xFinder_output";; "9",
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C FINE-TUNING HYPERPARAMETERS
When fine-tuning xFinder using the XTuner framework and the QLoRA method, all base models were trained with identical hyperparameters. The training was conducted on 8x A GPUs. We focused on the hyperparameters specified in Table 21 while all other hyperparameters were set to their default values.
Table 21: xFinder fine-tuning hyperparameter setting.
Hyperparameter
Setting
Batch Size
Maximum Length
2048
Learning Rate
2e-4
Pack to Max Length
True
Optimizer Type
Adam W
Betas
(0.9, 0.999)
Weight Decay
0
Warmup Ratio
0.03
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D DETAILED EXPERIMENTAL RESULTS
D.1 EXAMPLE OF EXTRACTION VS. JUDGMENT ACCURACY
For the concepts of "Judgment Accuracy" and "Extraction Aceuracy" mentioned in the main text, although we have provided explanations in the corresponding table notes, these concepts might still be somewhat difficult to understand. Therefore, we present real evaluation example by OpenCompass to help clarify these concepts.
"question": "Which pair don't reproduce the same way? Answer Choices: (A) (B) (C) cat and catfish (D) caterpillar and butterfly", "correct_answer", "C", (A) net the answer... So the correct answer is (D) Caterpillar and butterfly ...", "correct_ Keerreet_key__nsser"" ans_err"""" "D", lnnestedd_n_oouuuut_tta_a
"wrong",
Referring to the test data above, OpenCompass's extraction "A", which is inconsistent with the "correct answer", OpenCompass's judgment of the LLM's output is "Wrong". Comparing OpenCompass' 's extraction ("A"" with the "correct key answer" ("D"), we can conclude that Open- Compass's ' extraction is incorrect, which relates to extraction accuracy. Similarly, OpenCompass' judgment ("Wrong"" aligns with the "Is the tested LLM output right?" result ("Wrongg") meaning that OpenCompass" judgment is correct, which relates judgment accuracy.
100% reliable evaluation logic first extracts the key answer and then compares with the correct answer to make judgment. The comparison between the key answer and the correct answer is simple regular expression match, ensuring no errors. Under this assumption, correct extraction is necessary and sufficient condition for correct judgment. However, current evaluation methods are not 100% reliable, which means that even if an evaluation method fails to correctly extract the key answer, it may still "guess" the final evaluation result correctly. This directly leads to the judgment accuracy of the same evaluation method being higher than its extraction accuracy. The fundamental reason for the discrepancy between the two metrics extraction errors.
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D.2 EXTRACTION ACCURACY RESULTS
Table (2 presents the extraction accuracy of all 19 LLMs fine-tuned on the KAF generalization set. can be observed that LLMs with different parameter magnitudes achieve stable and high accuracy levels after training on the KAF training set.
Table 22:
Extraction Accuracy of xFinder fine-tuned on all 19 LLMs on the KAF test set. The first column lists the models fine-tuned using the KAF training set, including Qwenl.5-0.5B. Sweel.5-0.5B-Chrr Qwenl.5-1.8B, Dwenl.5-1.8B-Charr InternLM2- 1.8B, Gemma-2B, Gemma-2B- it, Qwen1.5-4B, Qwenl.5-4PP Beonl..5-B-Chie Chat, ChaGGLM3-6B-bas ChatGLM3-61 Qwenl.5-7B, Qwenl.5-
7B- 7B-Chat, InternLM2-7B,, Baichuan2-7B Bhhhtt Gemma-7B, Gemma-7B Llama-3-88 and Llama-
In8B-unstruct models.
xFinder
alphabet option
short text
categorical label
math
Overall
<Finder-qween1505
0.9735
0.9683
0,9805
0.9276
0.9688
x 3Finder-qwenl
0.9634
0.9635
0.9811
0.9351
0.9653
aFinder-qwenl151
0.9735
0,9781
0.9755
0.9457
0.9712
<Finder-qwen1S1 S1cchat
0.9709
0.9708
0,9736
0,9382
0.9673
1Finder-intermmm2288
0,9608
0,9695
0,9667
0,9201
0.9587
xFinder-gemma2
0.9698
0.9744
0.9774
0,9276
0.9673
Finder-gemmmaii
0.9661
0.9756
0.9755
0.9246
0.965
x xFindrr-qwen154
0,9730
0,9708
0,9698
0.9291
0.9657
x dFinder-qwenl 54chat
0,9671
0,9793
0,9686
0.9306
0.9647
x dFinder-chatglmmJbuse
0.9735
0,9793
0.9730
0,9291
0.9684
FFndeer-chaiglm36
0.9698
0.9769
0,9742
0.9261
0.9665
dFinder-qwen1577
0,9703
0,9744
0,9774
0,9306
0,9680
FFinder-gwenl/5cche
0.9735
0.9842
0.9748
0,9321
0.9702
Finder-intemlmm27
0.9740
0.9756
0.9730
0.9336
0.9686
x JFinucr-baichuun27chtt
0,9724
0.9756
0.9717
0.9276
0.9667
x xFinder-gemmal gemmaa7
0,9751
0,9805
0.9736
0.9367
0.9704
x IVinder-gemmaal
0,9682
0,9708
0.9226
0,9367
0.9498
Finder-llamas'
0.9698
0.9829
0,9692
0.9276
0.9661
0,9709
0,9683
0,9711
0,9382
0.9661
Table 23 displays the extraction accuracy, Aaces and Aace/ N of all 19 LLMSs fine-tuned on the KAF generalization set.
Table 23: Extraction Aceuracy of xFinder fine-tuned on all LLMs on the KAF generalization set,
along with Aace and /N values. The first column lists the models fine-tuned using the KAF training set: Qwen1.5-0.52 Qwenl.5-0.5BB-C.
Qwen Qwen1.5-1.8B
InternLM2-
1.8B, Gemma-2B, Gemma-2B-it. Qwenl.5-4p Owen1.5-4p 1.--B-Chat CharGLM3-66 basee ChatGLM3-
6B, Qwen1.5-7B, 1eeen1.5-7--hhh InterL 7B, Baichuan2- 7B-Chat, Gemma- 7B, Gemma-
it, Llama-3-8B, and -lama-3-BB-Instruee
XFinder
alphabet option
short text
calegorical label
math
Overall
Ance
Ance/N
<Finder-qwen1500
0.9477
0.9335
(1.988
0.9234
0.9342
0.0277
0.0554
WFinder-qwenl|55ccht 555chat
0,9289
0,9283
0,9163
0.9375
0.9255
0.0190
0.0380
xFindcr-qwenl511
0,9508
0.9439
0,9406
0.9500
0.9456
0.0391
0.0217
AFinder-qwenl111 81hhtt 518cat
0,9508
0.9356
0,9181
0.9531
0.9362
0.0297
0.0165
1Finder-intemlm218
0.9500
0.9407
0,8400
0.9344
0.9065
0
0
xFindcr-gemman
0.9484
0,9335
0,9337
0.9437
0.9393
0.0328
0.0164
xFinder-geen gemmmat
0,9344
0.9262
0,9263
0.9391
0.9304
0.0239
0.0120
0,9484
0,9345
0,9387
0.9484
0.9420
0.0355
0.0088
xinddr--qenn1 54ccht
0,9492
0,9407
0,9237
0.9531
0.9389
0.0324
0.0081
<finder-chatglmn33bsee
0.9484
0,9324
0,9481
0.9484
0.9449
0.0384
0.0064
<Finder-chatglm366
0.9563
0.9376
0.9163
0.9484
0.9369
0.0304
0.0051
xfindcr-qwen1557
0.9570
0,9345
0,9519
0.9391
0.9478
0.0413
0.0059
Winder-qween157chal
0.9563
0.9345
0,9319
0.9422
0.9409
0.0344
0.0049
<iinder-ineemm2227
0,9563
0,9563
0,9281
0.9375
0.9436
0.0371
0.0053
Frinder-baichuan2111
0,9477
0,9345
0,9431
0.9313
0.9409
0.0344
0.0049
Finder-gemmmal
0.9477
0,9345
0.9456
0.9500
0,9444
0.0379
0.0054
gender--emmaait 7it
0.9398
0.9200
0.8938
0.9469
0.9201
0.0136
0.0019
WFinder-llmaal
0.9563
0,9376
0.9469
0.9516
0.9482
0.0417
0.0052
0.9547
0,9428
0.9537
0.9547
0.9518
0.0453
0.0057
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D.3 EVALUATION IN WORLD SCENARIOS RESULTS
D.3.1 ALPHIABET& & SHORT TEXT OPTION TASKS
Table 24: Evaluating results of LLMs on the ARC-c (alphabet) task, presenting accuracy scores using
different key answer extraction methods.
LLMS
OpenCompass
UltraEval
Eval Harness
Wiande--lammaSSi
x/inder-gwen150t
Baichuan2-7B-Chan
39.17%
38.15%
52.80%
54.93%
55.04%
ChaGGLM366
56.56%
44.46%
56.26%
57.27%
57.17%
Gemm-2B-it
43.23%
42.22%
43.23%
44.56%
43.44%
Gemma-7B: 7B-it
50.76%
45.88%
35.10%
71.01%
70.80%
LnternLM--LLB-----
59.31%
59.21
58.70%
59.51%
59.61%
M2ternL 7B-Char
76.70%
71.72%
71.72%
77.92%
77.72%
Llama3-8B-Instre
78.03%
77.92%
77.92% %
78.03%
78.03%
Phi-2
66.12%
65.82%
70.91
70.91
71.01%
Dweel..5114B-CHH
83.32%
64.29%
76.60%
85,96 %
86.06
B.eel.5-MoB-A2.7BB 7B-hht
70.30%
67.96%
70.19%
73.35%
73.45%
Table 25: Evaluation results of LLMs on the ARC-c (text) task, presenting accuracy scores using different key answer extraction methods.
LLMs
OpenCompass
UltraEval
LM Eval Harness
xFinder-llama38i
xFFinder-qwen1505
Baichuan2-7B-Chn
40.80%
40.80%
47.50%
48.40%
48.40%
ChaGGLM3-GB
26.70%
26.70%
37.30%
40.70%
39.70%
Gemm Gemm-2B-ir 2B-it
8.00%
8.00%
25.10%
31.70%
29.60%
Demma-7B-ir
31.80%
31.80%
58.10%
65.20%
63.90%
MMcrrLL2-11.8B.
34.10%
34.10%
43.80%
44.30%
42.20%
InternLM2-7B-CCit
57.20%
57.20%
70.40%
71.00%
70.00%
Llama3-BB-Iostree
60.50%
60.50%
10%
74.80%
74.70%
Phi-2
41.30%
41.30%
48%40%
49. 39.80%
49. 50%
QwenL.5-14B-CCH
60.10%
60.10%%
77.80% %
80,60 %
80.60
41.30%
41.30%
61.10%
63.20%
62.50%
Table 26: Evaluation results of LLMMs the ARC-e (alphahet) task, presenting accuracy scores using different key answer extraction methods.
LLMS
OpenCompass
UltraEval
LM Eval Harness
Vlnnder-llam338i
XFinder-qwen1505
Baichuan2-7B--C
44.30%
42.70%
71.20%
74.10%
73.60%
Cha6GLM3-6B
71.40%
47.50%
70.60%
71.90%
72.10%
Gemm-2B-it
61.70%
59.50%
62.00%
62.80%
61.9%%
Gemma-7B-it
58.80%
54.80%
39.20%
84.40%
84.10%
MitemLLM-LL8B-00
74.90%
75.60%
76.00%
76.40%
76. 10%
InternLM2-7B-OL
89.40%
90.50%
90.20%
90.40%
90.50%
Llama3-8B-Instrue
92.70%
92.70%
92.70%
92.70%
92. 70%
Phi-2
78.00%
77.70%
84.20%
84.60%
84.60%
Qweni.5- 5-1BB-haat
93.40%
76.70%
88, 18.20%
95.60 %
95.60 %
Owenl.5-MoBBA2.7788
83.40%
81.20%
84.20%
86.10%
85.80%
Table 27: Evaluation results of LLMs on the ARC-e (text) task, presenting accuracy scores using different key answer extraction methods.
LLMS
OpenCompass
UltraEval
LM Eval Harness
NFinder-llama3Si Hamma38i
xVinder-qwen1500
Baichuan2-7B-Cha.
61.40%
61.40%
65.20%
67.00%
67.20%
ChatGLM3-6B
39.60%
39.60%
49.20%
52.70%
51.60%
Gemm-2B-it
8.20%
8.20%
32.80%
45.80%
41.20%
Gemma-7B-it
51.80%
$1.80%
76.80%
82.70%
81.60%
InternLM2-L.8 1----M-----B-
53.30%
53.30%
60.70%
60.50%
58.70%
InternLM2-7B-CC
79,40%
79.40%
86.50%
86.90%
86.20%
Llama3388-Instte
80.40%
80.40%
89,90% %
89.70%
89 40%
Phi-2
62.80%
62.80%
67.30%
69.50%
69.30%
Owenl.5-14B. 5-14B-Chat
74.10%
74.10%
88.50%
91.90%
91.80
Qwenl.5- 5-MoE-A2.75 7h--hha
56.70%
56.70%
75.80%
78.60%
77.60%
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Table 28: Evaluation results of LLMSs the Commonsense@QA (alphahet) task, presenting accuracy
scores using different key answer extraction methods.
LLMs
OpenCompass
UltraEval
LM Eval Harness
WFinder-llama38e
XFinder-gwen155t
Saichuan2-7B-CCn
36.00%
27.30%
58.60%
60.00%
59.90%
CharGL M3-6B
61.20%
31.80%
57.00%
62.30%
61.70%
Gemm-2B-it
44.70%
39.90%
44.90%
45.90%
45.00%
Demma-7B-it
55.10%
34.90%
41.00%
69.90%
69.90%
InternI.M2- M2-1.8B-CMB
71.50%
71.40%
73.70%
73.10%
72.90%
InternLM2-7B-CHi.
82,609
66.90%
83,70
83,70 %
83.80
-lama3--B-Iostren
71.60%
58.30%
71.70%
71.30%
71.30%
Phi-2
59.50%
51.90%
61.30%
61.40%
61.30%
Qwenl.5- 3-a4B-Chat
83.40%
60.20%
74.80%
83.60%
83.40%
Qwen1.5-Mon MMM.-o..B--CCCC
73.00%
57.80%
75.70%
77.70%
77.50%
Table 29; Evaluation results of LLMs the Commonsense@A (text) task, presenting accuracy scores
using different key answer extraction methods. LLMs
OpenCompuss
UltraEval
LM Eval Harness
x AVinder-llamu38ii
x/inder-qwen15k
Haichuan2-7B: Chah-hhar
51.20%%
31.20%
51.50%
52.40%
52.20%
ChatGLM3-6B
31.80%
31.80%
41.90%
46.60%
46.60%
Gemm-2BB 2B-it
5.70%
5.70%
31.40%
39.10%
38.10%
Gemma-7B-it
53.20%
53.20%
63.80%
68.00%
67.60%
MtternLM2-LL8B----
58.00%
58.00%
58.60%
59.00%
58.60%
I2ternLM2-7B--6
74.80
74.80%
75.80 %
78.50%
78. 00%
Llama3-8B-Instrue
70.20%
70.20%
71.4%
71.20%
71.50%
Phi-2
41.80%
41.80%
42.40%
42.60%
42.60%
Qwenl.5- 5-14B-Chut
70.50%
70.50%
75.60%
78.60%
78.90
Owenl.5-MoB-A2.
55,50%
55,50%
67.80%
70.40%
70.20%
Table 30: Evaluation results of LLMs on the MMLU (alphabet) task, presenting accuracy scores
using different key answer extraction methods. LLMs
OpenCompass
UltraEval
LM Eval Harness
x/inder-qwen1505
Baichuan2-7B-Char
36.60%
34.80%
41.30%
44.40%
44.30%
CharGLM3-GB
39.10%
31.20%
39.20%
39.40%
39.60%
Gemm-2B-it
37.40%
36.50%
37.50%
38.10%
37.30%
Gemma-7B-it
40.80%
35.40%
24.90%
50.00%
49.70%
InternLM2-1.888 Llt-hh
42%10%
44,30%
45.00%
45.80%
45.10%
InternLM2-7B-CC
54.10%
$4.80%
55.00%
55.70%
55.00%
Llama3-8B-Instie
62.50%
62.04)
62.70%
62.60%
62.50%
Phi-2
46, 10%
45.20%
48.30%
48 38.80%
48 60%
Qwenl.5-141 5-14B-Chat
65.40%
49.70%
59.80%
66.00% %
66.00 %
Qwenl.5-MoB-AN
50.80%
48.60%
51.60%
54.50%
54.20%
Table 31: Evaluation results of L.MSs the MMLU (text) task, presenting accuracy scores using different key answer extraction methods.
LLMs
OpenC
Ultrakval
LM Eval Harness
xF
x xFinder-qwen1506
Baichuan2-7B-Char
26.00%
26.00%
37.20%
35.70%
35.90%
ChaiGLM3-6B
13.40%
13.40%
24.00%
22.70%
22.40%
Gemm-2B-it
5.20%
5.20%
23.20%
24.50%
21.50%
Gemma-78B 7B-it
19.60%
19.60%
44.00%
42.30%
41.70%
InternL M--hh--hh
22.30%
22.30%
33.30%
29.50%
29.80%
MiternLM-77B-hhit
30.10%
30.10%
48.90%
44.60%
44.80%
Llama3-8B-Inntrut
36.80%
36.80%
55.20%
69.60%
49 80%
Phi-2
22.30%
22.30%
32.90%
30.60%
30.70%
Qwenl.5 5-14-Chaat
31.60%
31.60%
$3. 10%
52.60%
51.90 %
Qwenl.5- 1...5MMo--22.--CC
43.60%
41.80%
41.30%
26

---

Published as a conference paper at ICLR 2025
Table 32: Evaluation results of LLMs the OpenbookQA (alphahet) task, presenting accuracy
scores using different key answer extraction methods.
LLMs
OpenCompass
UltraEval
LM Eval Harness
xFinder- Ilamae--lama38it
XFinder-qwen150s
Baichuan2-7B -hahha
39.00%
37.00%
47.80%
50.40%
50.40%
ChaiGLM3-H6
52.80%
41.80%
52.00%
53.20%
53.20%
Gemm-2B-it
51,80%
51,00%
51.00%
52.60%
51.80%
Bemma-7B-it
57.40%
28.60%
49.40%
65. 65.20%
65.00%
InternI.M2- M2-1188-CMM
55.40%
56.80%
57.20%
56.20%
57.20%
InternLM2-7B-CHi
77.60%%
78.20%
78.00 %
78.40%
78. 60%
-lama3--B-Iostrun
74.60%
74.60%
74.40%
74.60%
74.60 60.
Phi-2
65,40%
64.60%
68.00%
68.00%
68.00%
Qwenl.5- 3-14B-Chat
83.00
68.20%
75.40%
84.20% %
84.20 %
Qwen1.5-Mon 5-.----2..7BB-- -..7-22BB-
69.80%
66.60%
69.20%
72.00%
71.60%
Table 33: Evaluation results of LLMs on the OpenbookQA (text) task, presenting accuracy scores
using different key answer extraction methods.
LLMs
OpenCompass
UltraEval
LM Eval Harness
sVinder-llamu38ii
xlinder-qwen1555
Haichuan2-7B: CBhhhhh
41.60%
41.60%
44.20%
45.40%
45.60%
ChatGLM3-6B
22.60%
22.60%
34.20%
36.20%
36.20%
Gemm-2B: 2B-it
5.20%
5.20%
24.00%
30.80%
30.60%
Gemma-7B-it
42.60%
42.60%
58.60%
63.40%
63.00%
InternLM2-L.88 L2-BBB-httt
38.40%
38.40%
44.00%
43.80%
42.20%
M
61.40%
61.40%
70.20%
70.00%
70.00%
Llama3-8B-Instrue
67.80%
67.80%
72.80%
72.80%
72. 60.
Phi-2
34.60%
34.60%
36.60%
37.40%
37.40%
Qwenl.5- 5-14B-Chut
68.00%
68.00%
75.40%
81.00%
80.80 %
Qwenl.5-M6B-A2.
39,60%
39.60%
54.20%
55.40%
55.20%
Table 34: Evaluation results of LLMs the SIQA (alphabet) task, presenting accuracy scores using
different key answer extraction methods.
LLMs
OpenCompass
UltraEval
LM Eval Harness
Winder-llammale x/inder-qwen1505
Baichuan2-7B-Chi
44.50%
43.50%
58.90%
60.40%
60.20%
CharGLM3-GB
66.10%
60.30%
66.00%
66.50%
66,50%
Gemm-2B-it
53.00%
52.70%
53.00%
53.40%
53.00%
Gemma-7B-it
63.00%
15.10%
55.40%
66.00%
66.30%
MacrnLM2-L.8B---
69. 69.20%
69, 80%
70.80%
69.80%
70.00%
InternLM2-7B-CC
75.90%
77.30%
77.70%
77.10%
77.20%
Llama3-8B-Instie
71.00%
70.50%
70.90%
70.60%
70.70%
Phi-2
63.60%
63.60%
64.00%
64.00%
64.00%
Qwenl.5-14p 5-14B-Chat
79.00%
58.30%
62.40%
78.90%
79.10%
Qwenl.5-MoB-Ae
68.90%
67.30%
71.60%
72.50%
72.50%
Table 35: Evaluation results of LLMSs the SIQA (text) task, presenting accuracy scores using
different key answer extraction methods.
LLMS
OpenC
Ultrakval
LM Eval Harness
Winder-llamatee
x xiinder-qeen1505
Baichuan2-7B-Char
50.00%
50.00%
52.90%
33.70%
33.50%
Cha1GLM3-6B
32.40%
32.40%
49.70%
52.50%
52.90%
Gemm-2B-it
8.40%
8.40%
35.20%
45.30%
43.90%
Gemma-7BB 7B-it
32.50%
32.50%
62.60%
64.00%
64.30%
InternLM2-1 M--hh--hh
53.10%
53.100
60.20%
59.60%
59,80%
MitenLLM-77B-hhit
58.40%
58.40%
68.609 606
67.70%
00.
Llama3-8B-Inntrute
61.30%
61.30%
64.90%
64.60%
64,70%
Phi-2
43.50%
43.50%
44.20%
45.80
45.70%
Qwenl.5 5-14-Chaat
62.50%
62.50%
68.80%
72.90%
72.70 %
Qwenl.5-X W...5-MM--A2.--CCC
62.70%
66.10%
65.40%
27

---

Published as a conference paper at ICLR 2025
Table 36: Evaluation results of LLMS the hellaswag (alphabet) task, presenting accuracy scores
using different key answer extraction methods.
LLMs
OpenCompass
UltraEval
LM Eval Harness
XFinder- 11ama-
XFinndr--wen11555
Baichuan2-7B-Char
46.40%
43.80%
46.80%
47.20%
46.70%
Cha-GLM3-6BB
44.50%
43.60%
43.90%
44.20%
43.80%
Gemm 2B-it
29.80%
27.80%
29. 29.80%
29.80%
29.70%
Gemma- Bemma-7B-it
50,20%
21.30%
42.30%
51.20%
51.10%
InternI M2-1L88 1-8t-hhtt
61.20%
62.20%%
60.30%
61.80%
61.30%
InternLM2-78 M2-7-Chtt
73.40%
72.70%
73.40%
73.40%
73.20%
69.30%
68.80%
69.30%
68.80%
68.80%
Phi-2
34.40%
34.20%
34.40%
34.30%
34.20%
Qwenl.5 5-14B-Chat
78.50%
75.60%
75.50%
78.60 %
78.60 %
Qwen1.5-Mool Mooo-..-M 7B-Chat
62.60%
59.70%
63.60%
66.10%
65.50%
D.3.2 CATEGORICAL LABEL OPTION TASKS
Table 37: Evaluation results of LLMs on the Amazon task, presenting accuracy scores using different key answer extraction methods. LLMs
OpenCompass
UlrraEvll
LM Eval Harness
x Winder-llama-5e
xFinder-qwen1155
Baichuan2-7B Chhhha
83.40%
83.40%
83.60%
83.70%
33.00%
ChatGLM3-6B
86.50%
86.50%
95.20%
95.20%
95.20%
Bemm-2B-it
81,50%
81.50%
85.30%
85.10%
85.30%
Gemma-7B-
73.60%
73.60%
94.10%
94.30%
94.20%
MnternL2-11.8B-B1
96.40%
96.40%
96.60 %
96.50 %
95.50%
InternLM2-7BB-CC
96.10%
96.10%%
96.20%
90%
96.10
Llama3-8B-Iostren
93.40%
93.40%
93.40%
93.40%
93.40%
Phi-2
91.60%
91.60%
91.80%
91.80%
91.80%
Qwenl.5-14B-CUL
95.50%
95.50%
95.80%
95.50%
70%
Sweo..5-MoE
90.50%
90.50%
93.70%
92.50%
92.60%
Table 38: Evaluation results of LLMs on the BoolQ task, presenting accuracy scores using different
key answer extraction methods.
LLMs
OpenCompass
UltraEval
LM Eval Harness
xFinder-qwen Findeer-llmmaBBi 1lama38it
Baichuan2-7B-Cha
63.60%
63.60%
63.00%
63.60%
63.60%
ChatGLM3-GB
20.60%
20.60%
60.50%
60.20%
61.00%
Gemm- 2B-it
24.80%
24.80%
63.50%
63.20%
63.80%
Gemma-7B-it
68.60%
68.60%
69.40%
69.70%
69.70%
BacrnLM2-1.8B----
55.00%
55.00%
64.60%
61.50%
58. 58.20%
InternLM2-7B-CRB
76.70%
76.70%
76.70%
76.50%
76.50
Llama3-8B-Iostree
68 60%
68.60%
68.50%
68.60%
68 60.
Phi-2
64.10%
64.10%
64.10%
64.10%
64.10%
Qwenl.5-1ed 5-14B-hhat
41.90%
41.90%
74.60%
74.90%
74.90%
Owenl.5-MoB-Ae A.BB--B-B
24.30%
24.30%
63.60%
63.50%
63.60%
Table 39: Evaluation results of L.LMS on the QNLI task, presenting accuracy scores using different
key answer extraction methods. LLMs
OpenCompass
UltraEval
LM Eval Haruess
Windee-llama3Sii
sFFinder-gwen150s
Baichuan2-7B-
58.40%
58.40%
58.40%
58.50%
58.50%
ChatGLM3-GB
42.60%
42.60%
63.50%
64.90%
65.50%
Gemm-2B-it
54.30%
54.30%
55.40%
55.40%
55.20%
Gemma-7B- 7B-it
53.30%
53.30%
56.80%
56.80%
57.70%
InternL ---
51.50%
51.50%
69.50%
67.60%
68.40%
InternLM2-7B-- 7h-hhhh
60.80%
60.80%
61.00%
60.80%
67 67.80%
Llama3-8B Ma-na3-BB-Instuue
72.10%
72.10%
72.10%
72.10%
72.20%
Phi-2
48.40%
48.40%
48.40%
48. 40%
48 40%
Qwenl.5-1-4 5-1BB-Cha
37.20%
37.20%
79.70%
78.00 %
79.60 %
Qwenl 5-MoB-A2 A227-A2h
35.80%
35.80%
77.00%
76.80%
17%10%
28

---

Published as a conference paper at ICLR 2025
Table 40: Evaluation results of L.LMs the WiC task, presenting accuracy scores using different key
answer extraction methods.
LLMs
Compsss
Evaa
LM Eval Haruess
xFinder-lammaBir
XHinder-gwenI50:
Baichuan2-7B-Chr
53.92%
53.92%
53.92%
53.92%
53.92%
ChatGLM3-60
32.29%
32.29%
51.88%
52.35%
52.35%
Gemm-2B-it
$4.39%
54.39%
54.39%
54.39%
54.39%
Gemma- 7B-i1
50.31%
50.31%
50.31%
50.47%
50.47%
InternI M2-1.81-Chr
54.08%
54.08%
54.08%
53.76%
54,08%
MierrLMM-7BBCMM
51.57%
51.57%
51.57%
51.57%
51.57%
I.ama3 Instructt
56.90%
56.90%
56.90%
56.90%
56.90%
Phi-2
50.31%
50.16%
50.31
Qwent.5- Baanl..--1--Che
47.65%
47.65%
64.42%
64.42%
64.42%
Qwenl -..7--2.78---
45.45%
45.45%
57.99%
57.84%
57 57.84%
D.3.3 MATH OPTION TASKS
Table 41: Evaluation results of LLMs on the GSM8K task, presenting accuracy scores using different key answer extraction methods.
LLMS
OpenCompan Ultrakval
LM Eval Harness
X Findee-llamaaSNi
X xFinder-qwen155
Raichuan2-77 2-7B-Chl
6.60%
5.50%
5.50%
6.70%
6.80%
ChatGLM3-6B
43.60%
0.00%
37.10%
43.60%
43 43.60%
Gemm- 2B-it
7.90%
3.10%
3.20%
8.70%
7.90%
Gemma-7hh 7B-i1
35.70%
1.20%
33.30%
36.80%
36.60%
InternLM2 (arrmMM2-1.8011 12--..8-
14,00%
2.40%
23.90%
31. 11.80%
26 20%
Internl M2-7B-CM- Chat
49.50%
5.10%
50.80%
63.10%
58 60%
Alamm33-8BIInsinn
77.00%
2.20%
80.30%
80.30%
Phi-2
15.60%
12.40%
13.40%
16.80%
16.50%
Qwenl 1--44--Chht
74.40%
0.40%
76.00%
77.00%
76.70%
Owenl.5-MoB-A2..
3.80%
42.10%
48.00%
48.20%
Table 42: Evaluation results of LLMs on the MATH task, presenting accuracy scores using different
key answer extraction methods. LLMs
OpenCompass
UltraEval
LM Eval Haruess
xFinder-lamaaeee Ilama3Sit
sFFnddergween150
Baichuun2-7B..
6.39%
6.39%
5.68%
7.20%
7.10%
ChatGLM3-6B
23.02%
8.22%
4.56%
23.02%
22.82%
Gemm-2B-. 2B-it
6.39%
4.56%
5.07%
8.82%
8.82%
Gemma-7BB 7B-it
13.18%
1.72%
11.46%
14.40%
14.10%
Interni (2--1.B--hhl
11.16%
3.55%
8.32%
11.66%
10.55%
Internl M2-7B-CCLLBB | Chat
19.57%
4.67%
16.23%
21 21.20%
20.39%
Llama3-8B- -B-ssrue
30.73%
7.10%
24.44%
30.63%
30.73%
Phi-2
9.03%
8.82%
6.90%
10.24%
9.33%
Swenl.5-14B-Chha
31.34%
4.26%
26.06 %
36.71 %
36.31 %
Qwenl.5- MMMo--2.7B-CL
17.65%
7.91%
6.69%
22.21%
22.52%
Table 43: Evaluation results of LLMs on the MultiArith task, presenting accuracy scores using different key answer extraction methods.
LLMs
OpenCoompass
UltraEval
LM Eval Harness
xFinder-llmaaAli
xFinder-qwen150s
Taichuan2-7B-Chon
5.00%
3.33%
3.33%
5.00%
5.00%
CharGL M3-6B
72.78%
1.11%
63.33%
72.78%
72.78%
Gemm-2B-it
23.89%
4.44%
4.44%
26.11%
23.89%
Gemma-7Ba 7B-it
84.44%
1.67%
77.78%
84.44%
83.33%
Internl M2-1.8 12-1188B-hha
30.56%
16.67%
36.67%
59.44%
45.56%
InternLM2-78. 142-7B--Cha Ch-Char
56.67%
36.67%
67.78%
81.67%
70.00%
Llama3 8B-InBtrucc
97.78%
1.67%
97.78%
97.78%
97.78%
Phi-2
33.33%
29.44%
28.33%
41.67%
38 89%
Sweel.5-144--4
98.33%
98.33%
98.33%
98.33
Qwent 5 MoB-A2.RP A2.7B Chat
62.78%
7.78%
52.78%
63.33%
63.33%
29

---

Published as a conference paper at ICLR 2025
E
BUMP CHARTS OF ALL TASKS
E.1 ALPHABET & SHORT TEXT OPTION TASKS
(elphabet)
Qwen15
Owen1.5 1-111.5-.----- Chae
Gemme-7B4:
ChatSur3
Gemm 28
10
iFiddenim33itt sfhder-qwe150
Evalss
OpenCossass
Ultra
ARC-c (taxt)
Lama3 68 Instruct
Qwena.5-248 (oaaa--------- char
IntemLM2 78 Chet
PM
BunKs
Gemme- 7B-
ChonGL 68
Germ 28-tt
Xinder lbm338 Wirder awen1505
Hvals
OpenComposs
Utaival
Figure 6: Bump Chart of ARC-c
(alphabet)
instrctt
Intemi MO-TBB-hht
Owen1 5-MoB-22 Cha
Phizz
IntemLMM2 189 Choz
Gemm 28
Gemma-7h4
ChanGLMa-c8
Ultretoal
ARC-e (text)
Lama3--8--iee 3---tt--eetuuct
IncomLM2 78 Coat
Chat
Qwend A2.J8-chut
IntamiMt-1.ck
Gemma 78
ChetGu93340
10-
Gemm-o2-44
505 UM Eusl
OpenCompass
Figure 7: Bump Chart of ARC-e
30

---

Published as a conference paper at ICLR 2025
Commonsensa@A (alphabet)
intemiM2 189 Ches
IntemiM2 7B Chat
Owen1 5.5-4----000
Liama3
Camma-7ib4
ChwisLM/3-18
10
Baictean2-7B-Chee
47 #hnde--emm354 Winde-qwen30
IN bollttensss
OpenCompers
Ulrebval
CommonsensaAA (text)
IntamLM--7BBCo
Qweni. 5-148 Char
L LamoB 58 Instruct
Intwnn (D-1 IBB-Cht
Owent 5-MeE-42 7B-Chat
Gamma- 78-x
Beidhaand-7B-Chht
Psk-
ChanGLMB
Figure 8: Bump Chart of CommonsenseQA
MMLU (alphabet)
Limma instruct
IntemLMM2 78 Chat
Swen1 5-140tthhe
Gamma- B4:
10
Chatsura
xF <hnderrlam35-tt sinder-swwnn308
UR Biatsaness
OpenCimsass
Ultravvl
(text)
Owent
IntemLMB-78- 7e-Chet
Be teichaand-7BB-hh
Qwent: 5-Mee-442 7D-Chet
InComLM2. 1.58 Chat
PMj.
Gemma-77D-
ChinGLMB-6R
Figure 9: Bump Chart of MMLU
31

---

Published as a conference paper at ICLR 2025
OpenbookQA (alphabet)
IstemiM2 7B Chat
Liama3 instrutt
Owen1 5-344----hh
Owen1. 5-MoEA2.PPP
Baictunn2--B-0h
10
Demma-7B4
47 dhndee-fmm35-1 3inder-wwn130 IK Dolltee
OpenCompess
Ulrebval
OpenbookOA (text)
Owent 5-141-----88---
ama8 68 Instruct
IncomuM2 78 Chot
Gemme-7I-
Beirhann--TB-Cht
Owena Mod A2.78-Chut
IntamiM2-1 SB-Chhtt
Psi-
ChanGLMB
OpenCompass
Figure 10: Bump Chart of OpenbookQA
SIQA (alphabet)
IntemLMM 78 Chat
Laama instruct
Interni 90-1 nB-Crat
Qwen1.5-148
Gamm-2844
10
Gemma 78
xF <hnderrlam35-it siinde-gwwnn308
UK Biatsaness
OpenCimsass
Ultravvll
(taxt)
Qwend.5
ama3-68-tnetruct
IntemLMB-78- Chen
IntamiM0-1 sB-Chat
Beichuand-/B-Chtt
Owen1.5 MoE 42.78 Chat
Gemma-70--
ChinSLMB-69
Figure 11: Bump Chart of SIQA
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(alphabet)
IntameMt-78 Coas
Lama3-B8-tratrud
IntomuM2 1.88 Chat
Owwnt S-MeE-A2 7D-chat
Beichann2-BB-Chtt
ChatGLMB 68
Psk-
Gemm 28-1t
Gemma
llama38 owen1505
Hamess
OpenCompase
UtriEval
Figure 12: Bump Chart of hellaswag (alphabet)
E.2 CATEGORICAL LABEL OPTION TASKS
Qwenl.
Owent 5-MKE-22 7B-Chat
ChatGu93365
Be Beichannd-7B-Chu
Gemm 28
Gemma 78
dinderlama28.4 WRnnder owen1555
UM Evall Hamess
OpanCampass
UtrrEval
Figure 13: Bump Chart of Amazon
BaolQ
IntamuM2778 res
Gemma-7B
Lama3 s8 inctrcct
Pskk
Qwenl.
ChetGLM4366
xhrdeelimal0-4 xindee-owntt0 505
OpenCampass
UtraEval
Figure 14: Bump Chart of BoolQ
Lama3 58
Beichuand-77--Ca
Gemm-2844t
Gemmo 78
InCemuM2 1.58 Chat
Charci 91-tn
Qwent.5-14e cha:
Owend. 5-Mee 42.40-42.78.... 72-78-hhut
UK Eval itemas
Opentampass
Utratval
Figure 15: Bump Chart of QNLI
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Gemm-284t
InteriMt-- MB--het
Boldhuan2 78 Chat
Intenn M0-70M0-/B-C
Gemma-70--
PMk22
Owenä (area---------0 Chat
Owenl. 5-McE A2.77BChot
ChaRSLMB
llamm38 owen1505
Hamess
OpenCompase
UtriEval
Figure 16: Bump Chart of WiC
E.3 MATH OPTION TASKS
GSMBK
Psid
Baidhuan2 78 Chat
M2-78-Chet
Owent 5-MrE-A2 7B-Chat
Qwent
10-
ChaiGi 94-68
Khode-lbmat8-- WRnder-een1105
Evstismmess
OpenCompass
UtriEval
Figure 17: Bump Chart of GSM8K
ChanGLMB
Owent. 5-Met-12 70-chat
Lame se Instrutt
Baidhuan2 78 Chat
IntemmM0-7B:co
Gemm-28-4t
Owend. 5-148--Chh
Intwmi-M211 BB-Chet
1lmm23 awes1505
Hamess
OpenCompase
Figure 18: Bump Chart of MATH
MultiArith
IntemLM0-7B-CMM
Pskz
Intamii92-1 MB-Chet
Chot
Duncs
ChinGL 68
10-
Owenl. 5 Chor
XRnder lbmm33 xAndor dwen1505
OpanCompass
Utrivall
Figure Bump Chart of MultiArith
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F PROMPTS
F.1 PROMPTS FOR LLM RESPONSES GENERATION & REAL- WORLD SCENARIOS EVALUATION
In these figures, the red part represents the System prompt, the blue part represents the Demonstration, and the black part represents the Question.
You expert in commonsense reasoning, Please choose the from options A sponding to the question.
Q: Coal is fossil fuel that is formed from Answer Choices: (A) water eroding the land. (B) meteors hitting Earth. (C) repeated volcanic eruptions. (D) the decay of organic material.
A:
Figure 20: O-shot prompt for LLM response generation (using alphabet option type question as an example).
You expert in commonsense reasoning, Please choose the answer from options A to D, corre- sponding to the question.
Q: Coal is a fossil fuel that formed from Answer Choices: (A) water eroding the land. (B) metcors hitting Earth. (C) repeated volcanic eruptions. (D) the decay of organic material. A:
End your final answer with 'The answer is <answer>."
Figure 21: restrcc prompt for LLM response generation & Real-World scenarios evaluation (using alphabet option type question as an example).
You expert in commonsense reasoning, Please choose the answer from options A to D, corre- sponding to the question.
Q: Coal is a fossil fuel that formed from Answer Choices: (A) water eroding the land. (B) meteors hitting Earth. (C) repeated volcanic eruptions. (D) the decay of organic material. A:
Let's think step by step.
Figure 22: 0-shot-cot prompt for LLM response generation (using alphabet option type question as an example).
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You expert in commonsense reasoning, Please choose the answer from options A to D, corre- sponding to the question.
Q: Coal is a fossil fuel that formed from Answer Choices: (A) water eroding the land. (B) meteors hitting Earth. (C) repeated volcanic eruptions. (D) the decay of organic material. A:
End your final answer with "The answer is <answer>."
Let's think step by step.
Figure 23: 0-shot-cotrrestrict prompt for LLM response generation (using alphabet option type question as an example).
You expert in commonsense reasoning, Please choose the answer from options A to D, corre- sponding to the question.
*4**8 Start In-Context Examples
Q: Which of the following helps to produce urine in humans and other mammals? Answer Choices: (A)
hladder (B) urethra (C) kidneys (D) ureter
A: The answer is (C).
Q: An atom consists of nucleus surrounded by Answer Choices: (A) ions. (B) protons. (C) neutrons.
(D) electrons.
A: The answer is (D).
Q: Which lists the diameter of the planets in order from smallest largest? Answer Choices: Venus, Earth, Mercury, Mars (B) Earth, Mars, Venus, Mercury (C) Mars, Mercury, Earth, Venus (D)
Mercury, Mars, Venus, Earth
A: The answer is (D).
Q: In container, mixture of water and salt stirred that the salt dissolves completely. Sand added to this solution allowed to settle bottom
container. the container placed on heat source and the liquid evaporates completely, what will be left the container? Answer Choices: (B) Only salt will remain in the container. (C) Only sand will remain in the container. (D) Salt and sand will both remain in the container.
(A) Nothing will remain in the container.
A: The answer is (D).
Q: Which instrument measures atmospheric pressure? Answer Choices: (A) barometer (B) hygrometer
(C) thermometer (D) magnetometer
A: The answer is (A).
***** End In- In-Context Examples #****
Q: Coal is a fossil fuel that formed from Answer Choices: (A) water croding the land. (B) metcors hitting Earth. (C) repeated volcanic cruptions. (D) the decay of organic material. A:
Figure 24: 5-shot prompt for LLM response generation (using alphabet option type question as an example).
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You are an expert in commonsense reasoning, Please choose the answer from options A to D, corre- sponding to the question.
***8e Start In- mn-Context Examples *****
Q: Which the following helps to produce urine in humans and other mammals? Answer Choices: (A)
bladder (B) urethra (C) kidneys (D) ureter
A: The answer is (C).
Q: An consists of nucleus surrounded by Answer Choices: (A) ions. (B) protons. neutrons.
(D) electrons.
A: The answer is (D).
Q: Which lists the diameter of the planets in order from smallest to largest? Answer Choices: (A) Venus, Earth, Mercury, Mars (B) Earth, Mars, Venus, Mercury (C) Mars, Mercury, Earth, Venus (D) Mercury, Mars, Venus, Earth
A: The answer is (D).
Q: In container, mixture of water and salt is stirred so that the salt dissolves completely. Sand is added to this solution and allowed to settle to the hottom of the container. If the container is placed on heat source and the liquid evaporates completely, what will he left in the container? Answer Choices: container. (C) Only sand will remain in the container. (D) Salt and sand will both remain the container.
(A) Nothing will remain in the container.
(B) Only salt will remain
A: The answer is (D).
Q: Which instrument measures atmospheric pressure? Answer Choices: (A) barometer (B) hygrometer
(C) thermometer (D) magnctometer
A: The answer is (A).
***** End In- In-Context Examples
Q: Coal is fossil fuel that formed from Answer Choices: (A) water eroding the land. (B) meteors hitting Earth. (C) repeated volcanic eruptions. (D) the decay organic material.
A:
End your final answer with 'The answer is <answer>."
Figure 25: 5-shot-restrict prompt for LLM response generation (using alphabet option type question as an example).
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You are an expert in commonsense reasoning, Please choose the answer from options A to D, corre- sponding to the question.
***8e Start In- mn-Context Examples *****
Q: Which the following helps to produce urine in humans and other mammals? Answer Choices: (A)
bladder (B) urethra (C) kidneys (D) ureter
A: The answer is (C).
Q: An consists of nucleus surrounded by Answer Choices: (A) ions. (B) protons. neutrons.
(D) electrons.
A: The answer is (D).
Q: Which lists the diameter of the planets in order from smallest to largest? Answer Choices: (A) Venus, Earth, Mercury, Mars (B) Earth, Mars, Venus, Mercury (C) Mars, Mercury, Earth, Venus (D) Mercury, Mars, Venus, Earth
A: The answer is (D).
Q: In container, mixture of water and salt is stirred so that the salt dissolves completely. Sand is added to this solution and allowed to settle to the hottom of the container. If the container is placed on heat source and the liquid evaporates completely, what will he left in the container? Answer Choices: container. (C) Only sand will remain in the container. (D) Salt and sand will both remain the container.
(A) Nothing will remain in the container.
(B) Only salt will remain
A: The answer is (D).
Q: Which instrument measures atmospheric pressure? Answer Choices: (A) barometer (B) hygrometer
(C) thermometer (D) magnctometer
A: The answer is (A).
***** End In- In-Context Examples
Q: Coal is fossil fuel that formed from Answer Choices: (A) water eroding the land. (B) meteors hitting Earth. (C) repeated volcanic eruptions. (D) the decay organic material.
A:
Let's think step step.
Figure 26: 5-shot-cot prompt for LLM response generation (using alphabet option type question an example).
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You are an expert in commonsense reasoning, Please choose the answer from options A to D, corre- sponding to the question.
$4*88 Start In-Context Examples *****
Q: Which the following helps to produce urine humans and other mammals? Answer Choices: (A)
bladder (B) urethra (C) kidneys (D) ureter
A: The answer is (C).
Q: An atom consists of nucleus surrounded by Answer Choices: (A) ions. (B) protons. (C) neutrons.
(D) electrons.
A: The answer is (D).
Q: Which lists the diameter of the planets in order from smallest to largest? Answer Choices: (A) Venus, Earth, Mercury, Mars (B) Earth, Mars, Venus, Mercury (C) Mars, Mercury, Earth, Venus (D)
Mercury, Mars, Venus, Earth A: The answer is (D).
Q: a container, mixture of water and salt stirred so that the salt dissolves completely. Sand added to this solution and allowed to settle to the bottom of the container. If the container is placed on heat source and the liquid evaporates completely, what will be left in the container? Answer Choices: (B) Only salt will remain in the container. (C) Only sand will container.
(A) Nothing will remain in the container.
remain the container. (D) Salt and sand will both remain
A: The answer is (D).
Q: Which instrument measures atmospheric pressure? Answer Choices: (A) barometer (B) hygrometer
(C) thermometer (D) magnetometer
A: The answer (A).
***8e End In- Co-Cetex Examples **4**
Q: Coal is fossil fuel that formed from Answer Choices: (A) water eroding the land. (B) metcors hitting Earth. (C) repeated volcanic eruptions. (D) the decay of organic material.
End your final answer with "The answer is <answer>."
Let's think step by step.
Figure 27: 5-shot-cot-restriict prompt for LLM response generation (using alphabet option type question an example).
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F.2 PROMPTS FOR AUTO LABELING USING GPT-4
As shown in Step 3 of Figure 3 we employed self-consistency strategy for the automatic annotation using GPT-4. Specifically, shown in Figure 28 and Figure 29) respectively. In these figures, the red part represents the System prompt, the orange part represents the Instruction prompt, and the black part represents the Question.
used two forms of prompts: normal form and XML form, as
You help assistant tasked with extracting the precise key from given output sentences. You must only provide the extracted key answer without including any additional text.
I will provide you with question, output sentences along with an answer range. The output sentences are the response of the question provided. The answer range could either describe the type of answer expected or list all possible valid answers. Using the information provided, you must accurately and precisely determine and extract the intended key answer from output sentences. Please don't have your subjective thoughts about the question.
If the output sentences present multiple different answers, carefully determine if the later provided answer is correction modification of a previous one. If extract this corrected modified answer the final response. Conversely, if the output sentences fluctuate between multiple answers without clear final answer, you should output [No valid answer].
For a question of type "classify_ted __ttttt'," if the output sentences do not clearly provide any answer within the specified answer range, then the output should be [No valid answer]. Additionally, if the answer range list and the answer given in the output sentences not included in the answer range, also output [No valid answer].
Question: {question]
Output sentences: (LLM response]
Key answer type: (key answer type]
Answer range: [answer range)
Key extracted answer:
Figure 28: The prompt for automatic annotation by GPT-4: normal form.
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You help assistant tasked with extracting the precise key answer from given output sentences. You must only provide the extracted key answer without including any additional
I will provide you with question, output sentences along with an answer range. The output sentences are the responses to the question provided. The answer range could either describe the type of answer expected or list all possible valid answers. Using the information provided, you accurately and precisely determine and extract intended key answer from output sentences. Please don't have your subjective thoughts about the question.
If the output sentences present multiple different answers, carefully determine if the later provided answer is correction modification of previous onc. If extract this corrected modified answer the final response. Conversely, the output sentences fluctuate between multiple answers without clear final answer, you should output [No valid answer).
For a question of type "classify_ text", if the output sentences do not clearly provide any answer within the specified answer range, then the output should be [No valid answer]. Additionally, if the answer range is list and the answer given in the output sentences not included in the answer range, also output [No valid answer].
<?xml version="1.0" ?>> <information>
<question (qq=ssi"">qqeeetoon (cee </question>
<output_sentennet sentences type="str">(LII response <<outputsseeteeness>>>>>>>>> sentences>
dicy_answer_type "ypp="str><(kg) answer //ke) <peeaaserees <answer range type= "dict>>>>>>>
<type type="str" "str"a list of all possible valid answers</type>
<choices type= "list""l""" > [answer range range]<<chools <Ichoices>>
</answer <lanswerrrange>
</infomation>
Key extracted answer:
Figure 29: The prompt automatic annotation by GPT-4: XML. form.
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F.3 PROMPTS FOR JUDGE MODELS USING GPT-4
As shown in Figure 30 this is the prompt used by GPT-4 as Judge in our experiments. This prompt is also applied other judge models used for comparison in our study. Figure 31 illustrates the CoT prompt used by GPT-4 as Judge (CoT).
You are diligent and precise assistant tasked with evaluating the correctness of responses.
We request your feedback whether the model's response correctly answers the user question above.
Based the reference answer, please determine the model's answer is correct. Please first output "correct" or "incorrect" on single line. the subsequent line, provide a brief explanation of your judgment, ensuring it is objective and clear.
Question: {question]
Reference Answer: (reference)
Model's Answer: (answer)
Figure 30: The prompt for GPT-4 as Judge.
You are diligent and precise assistant tasked with evaluating the correctness of responses. Think step by step you make your evaluation.
We request your feedback whether model's response correctly answers the user question above. Follow these steps to make your evaluation:
1. Think step by step: Read the user question carefully.
2. Think step by step: Review the reference answer and understand the key points it covers.
Think step by step: Compare the model's answer with the reference answer.
Think step by step: Determine the model's answer addresses the key points in reference answer
and correctly answers the question.
First, provide your reasoning in detail. Then, clearly state your judgement either "Correct" or "Incorrect."
Please present your response in the following JSON format:
{1
"reasoning": "Your step-by-step reasoning here.",
"judgement": "Correct Incorrect"
}
Question: (question]
Reference Answer: (reference)
Model's Answer: [answer]
Figure 31: The prompt for GPT-4 Judge (CoT).
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F.4 PROMPTS FOR EXTRACTING KEY ANSWERS USING XFINDER
As shown in Figure 32 this is the carefully designed prompt template we used for extracting key answers with xFinder.
You are help assistant tasked with extracting precise key answer from given output sentences. You must only provide the extracted key answer without including any additional text.
I will provide you with question, output sentences along with an answer range. The output sentences the response of the question provided. The answer range could either describe the type of answer expected or list possible valid answers. Using information provided, you must accurately and precisely determine and extract the intended key answer from the output sentences. Please don't have your subjective thoughts about the question. First, you need to determine whether the content of the output sentences is relevant to the given question. If the entire output sentences are unrelated to the question (meaning the output sentences are not addressing the question), then output [No valid answer). Otherwise, ignore the parts of the output sentences that have no relevance to the question and then extract the key answer that matches the answer range. Below are some special cases you need to be aware of:
(1) the output sentences present multiple different answers, carefully determine if the later provided answer is correction or modification a previous one. extract this corrected modified answer as the final response. Conversely, the output sentences fluctuate between multiple answers without clear final answer, you should output [No valid answer].
(2) If the answer range is list and the key answer in the output sentences is not explicitly listed among the candidate options in the answer range, also output [No valid answer).
Question: [question]
Output sentences: (LLM response]
Key answer type: (key answer ]ype)
Answer range: (answer range)
Key extracted answer:
Figure 32: The prompt for extracting key answers by xFinder.
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Reviews:
[{"review_id": "RXMkvtQnDN", "reviewer": "Reviewer_B8RQ", "summary": "This paper proposes a novel evaluator for answer extraction and matching in LLM evaluation. The main idea is to first construct a large-scale LLM response evaluation dataset, and then train (small) LLMs on it. This paper conducts an extensive evaluation of multiple tasks with comparison with multiple LLM-based evaluators.", "questions": "Please see above.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1 A large dataset that can be used for further LLM-based evaluation.\n2 A new model that can be used for more reliable evaluation.", "weaknesses": "To be perfectly honest, I am not an expert in LLM-based evaluation. But to me, the main contribution is the construction of a dataset that can help train LLM evaluators, with the help of other LLMs (e.g GPT-4). Thus the novelty of the proposed model is less convincing as it does not provide any new architecture. Training LLMs on evaluation data as evaluators have also been explored in previous research, such as [1] and its subsequent work. Could the authors explain more on its novelty? For example, in terms of training process, and model architectures, how does the xFinder differ from previous work that trains LLMs on evaluation data? Also, would it possible to prompt GPT-4 or other very big LLM using ICL with the constructed data, and how would it perform?\n\n[1] Towards a Unified Multi-Dimensional Evaluator for Text Generation", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "oH6boNmzC0", "reviewer": "Reviewer_rbE3", "summary": "This paper introduces xFinder, a novel evaluator designed for answer extraction and matching in the context of LLM evaluation. The study identifies the limitations of current answer extraction modules, particularly those based on RegEx, in handling inconsistencies in model response formats. To address these issues, the authors propose xFinder to enhance extraction accuracy and evaluation reliability. The authors developed a dataset called the Key Answer Finder (KAF) dataset to train xFinder. Experimental results demonstrate that xFinder significantly outperforms existing frameworks and model-based evaluators in terms of extraction accuracy and evaluation efficiency.", "questions": "see weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The identified challenges in LLM response extraction and matching are realistic and merit attention.\n\nThe paper proposes a novel method to improve answer extraction modules, addressing limitations in existing approaches.\n\nThe paper is well-structured, with a clear progression from problem definition to methodology and experimental analysis.", "weaknesses": "Although the KAF dataset is used to validate xFinder’s performance, the paper lacks a comprehensive exploration of the model’s generalizability to entirely different datasets and error types.\n\nThe paper does not include sufficient experimental analysis on the impact of xFinder on final evaluation outcomes, such as a comparison between using xFinder and other extraction methods in terms of evaluation results.\n\nThere is a lack of detailed analysis of the KAF dataset’s quality, such as inter-annotator agreement metrics.", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "spS0XjpdYQ", "reviewer": "Reviewer_vwGU", "summary": "The paper introduces xFinder, a tool designed to enhance the accuracy of evaluating large language models by improving key answer extraction and matching. It identifies flaws in current methods like test set leakage and RegEx limitations, proposes a new dataset (KAF) for training, and shows xFinder outperforms traditional and other automated judge models in extraction and judgment accuracy, thus contributing to more reliable LLM evaluations.", "questions": "What is the prompt you used for GPT-4 as Judge (CoT)?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The significance of accurate answer extraction in evaluations is often underestimated, yet it critically impacts results. This study rightly emphasizes this aspect.\n- xFinder demonstrates strong performance in accuracy over conventional RegEx frameworks.\n- Both the model and its dataset are immediately usable for enhancing the reliability of LLM assessments.\n- The paper effectively outlines the problems in current evaluation methods and introduces a well-structured solution.", "weaknesses": "- The techniques may not be applicable to responses where the answer is not a short, extractable phrase.\n- Although the results are promising, I suspect the technique might be replaced by stronger LLMs used as judges with improved prompting techniques in the near future, which could also generalize better for longer responses. The results in Table 3 are good, showing that even GPT-4 as a judge does not perform as well as xFinder. Therefore, I believe xFinder remains useful at this moment for tasks that have a similar distribution to its training data. It will also be interesting to discuss the combination of xFinder and other techniques.", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jov25Ztkn5", "reviewer": "Reviewer_XqZp", "summary": "The paper proposes a training dataset KAF for key answer extraction and the correspondingly trained models, xFinder. The motivation lies in improving the extraction of answers in LLM responses for more reliable evaluation. The authors create KAF based on various LLMs, different evaluation tasks, and widely used prompting techniques, i.e., CoT and few-shot prompting. Based on the comprehensive experiments in the paper, xFinder outperforms RegX and other LLM-based methods in answer extraction.", "questions": "- **Why does the key extraction task need to have ``short text`` and ``alphabet option`` categories, as the former could be transformed into the latter?**\n\n- **Does the order of the xticks in the bump charts affect the comparison between ``alphabet option`` and ``short text``?**\n\n- **What is the trade-off between xFinder and simply adding one or more regular expression patterns in RegX?**: It is known to us that LLMs are good at learning patterns and following instructions, which could have further enhancements as the models' reasoning capabilities are further enhanced. xFinder indeed makes good improvements, but where do they come from? We must be sure that xFinder (or future work) helps answer extraction better than RegX, such as finding answers from those nasty answer patterns. After all, it is easy to write RegX patterns nowadays by simply prompting GPT-4 and LLM-based methods are still inefficient compared to lexical methods.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "The paper is solid enough on all claims with its comprehensive experiments. The proposed KAF dataset is suitable for future research on developing more reliable evaluation systems. The xFinder models are more efficient and reliable than current LLM-based methods. In all, the paper did a good engineering research on using LLMs to better find the answers from their own responses.", "weaknesses": "- **Missing Related Work**: The work [1] is also highly related to this work, especially for the Judgement Accuracy part. It holds a similar idea by comparing different evaluation methods, including LLM-based ones, in directly evaluating open-question answering.\n\n- **Annotation Agreement**: Human rechecking is one of the significant parts of the data generation pipeline. What are the annotation agreements between annotators? \n\n- **Human/Case Study**: Except for those numbers in the experiment tables, it is important to do the case study on the output of xFinders. For example, **How and Why is xFinder better**, **Is it worth using xFinder other than RegX or other LLM-based methods?**, **Summarize the failure modes of those inferior methods and how xFinder could perform better in these cases.**, etc.\n\n- **Writing**: I recommend adding more content about experimental settings, such as evaluation metrics, baseline models, etc, to the main text. There are too many staff in the Appendix. Things could be clearly explained in the main text for better reading.\n\n(see the below ``Questions`` section for more)\n\n[1] Wang C, Cheng S, Guo Q, et al. Evaluating open-qa evaluation[J]. 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Author rebuttal:

Decision: Accept (Poster)