Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models
Authors: Daman Arora
Himanshu Gaurav Singh
Mausam .
Venue: EMNLP 2023 Main (2023)
Keywords: Reasoning
Large Language Model
Benchmark
Problem Solving
TL;DR: We propose a challenging benchmark dataset for reasoning consisting of questions from JEE Advanced exam and analyse the weaknesses of GPT-4 on our benchmark.

Abstract: The performance of large language models (LLMs) on existing reasoning benchmarks has significantly improved over the past years. In response, we present JEEBench, a considerably more challenging benchmark dataset for evaluating the problem solving abilities of LLMs. We curate 515 challenging pre-engineering mathematics, physics and chemistry problems from the highly competitive IIT JEE-Advanced exam. Long-horizon reasoning on top of deep in-domain knowledge is essential for solving problems in this benchmark. Our evaluation on various open-source and proprietary models reveals that the highest performance, even after using techniques like self-consistency, self-refinement and chain-of-thought prompting, is less than 40%. The typical failure modes of GPT-4, the best model, are errors in algebraic manipulation, difficulty in grounding abstract concepts into mathematical equations accurately and failure in retrieving relevant domain-specific concepts. We also observe that by mere prompting, GPT-4 is unable to assess risk introduced by negative marking for incorrect answers. For this, we develop a post-hoc confidence-thresholding method over self-consistency, which enables effective response selection. We hope that our challenging benchmark will guide future re-search in problem-solving using LLMs.

Have LLMs Advanced Enough?
A Challenging Problem Solving Benchmark For Large Language Models
Daman Arora*t itt
Himanshu Gaurav Singh"
Mausam IIT Delhi mausam @ ise.iit..ac.in
Microsoft Research
UC Berkeley himanshu singh @ berkeley.edu
daman 1009ur e
Abstract
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The performance of large language models (LLMs) on existing reasoning benchmarks has significantly improved over the past years. In response, we present JEEBENCH, a consid- erably more challenging benchmark dataset for evaluating the problem solving abilities of LLMs. We curate 515 challenging pre- engineering mathematics, physies and chem- istry problems from the highly competitive IIT JEE-Advanced exam. Long-horizon reason- ing on top of deep in-domain knowledge is essential for solving problems in this bench- mark. Our evaluation on various open-source and proprietary models reveals that the highest performance, even after using techniques like self-conse seff-consistency, self-refinement and chain-of- thought prompting, is less than 40%. The typi- cal failure modes of GPT-4, the best model, are errors in algebraic manipulation, diffieulty in grounding abstract concepts into mathematical equations accurately and failure in retrieving relevant domain-specifer concepts. We also ob- serve that by mere prompting, GPT-4 unable assess risk introduced by negative marking for incorrect answers. For this, we develop post-hoe confidence-thie thiisooice method over self-consistency, which enables effective re- sponse selection. We hope that our challeng- ing benchmark will guide future re- rearch in problem-solving using LLMs.
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Figure 1: An example problem from JEEBENCH
et al., 2021b), MMLU (Hendrycks et al., 2021a) and ScienceQA (Lu et al., 2022)
1 Introduction
Rising capabilities of LLMs call for harder benchmarks. We introduce JEEBENCH, a bench- mark consisting of 515 problems that require com- plex logical and mathematical reasoning on top of deep in-domain knowledge of pre-engineering level Physics, Chemistry and Mathematics. Prob- lems have been curated from the past 8 editions of the Joint Entrance Examination (JEE)-Advanced exam, held annually in India as an entrance test to India's 's premier engineering institutes: the IITs. The exam is designed to be consuminn,, diffi-
The capabilities of large language models (LLMs) have been improving since the last decade on plethora of tasks including reasoning. Most re- cently, GPT-4 demonstrates significant improve- ments over GPT-3 on tasks such as code-generation, arithmetic and commonsense reasoning (Bubeck et al., 2023), exhibiting impressive performance on standard reasoning and STEM benchmarks such as GSM-8K (Cobbe et al., 2021), MATH (Hendrycks
cqual contribution, work done while at IIT Delhi

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cult, and has low selection rate (approx. 5%). The problems in the dataset require complex interplay of employing multiple high-level domain specific concepts, grounding them into mathemat- ical equations/constraints followed by algebraic manipulation and arithmetic operations. Figure is a problem from the dataset along with an expert's solution. In this problem, the ideal solution in- volves the retrieval of the appropriate concepts: the rules of static equilibrium, grounding the concepts into mathematical equations for the specific prob- lem instance, followed by solving the equations in order to find the final answer. Other instances of domain-specifi concepts can be Balancing of redox reactions (Chemistry), Current into a junc- tion equals current out of the junction (Physics) and Integration by parts (Mathematics). More such examples can be found in the Appendix A.2.
The JEE Advanced Exam comes with the bane of negative marking for incorrectly answered ques- tions. This makes the exam even more challeng- ing, because in addition to advanced problem solv- ing skills, it requires an accurate risk assessment and computing good policy based on it. Our ex- periments demonstrate that when prompted with the marking scheme, GPT-4'ss performance actu- ally drops. To mitigate this, we employ simple method thresholding over self consistency. Self consistency generates multiple responses for each question. Relative frequency in the set of responses can be considered as a proxy for confidence score of each option. Threshold on the confidence score can be tuned using a validation set. We find that GPT-4's score, after augmenting it this way, lies in the top 10-20 percentile of human scores in the 2023 edition of the exam.
Overall, we hope that this benchmark serves as strong and reliable test-bed and fosters future research on problem solving with LLMs. Our code and dataset are available at https: http://ggttttheccooee thwb. com/ dair-iitd/jeebien jeebench.
We conduct a qualitative and quantitative study of contemporary open-source and proprietary LLMs these problems and also highlight av- enues for further research. Our analysis indicates that GPT-4 is unparalleled in performance com- pared to other models. It demonstrates long hori- zon reasoning and the ability to manipulate com- plex algebraic equations in quite a few problems. We observe that chain-of-thought prompting (Ko- jima et al., 2022) and self-consistency (Wang et al., 2023b), which are recent proposals to improve LLM performance, are indeed effective on our dataset.
Related Work
Reasoning has been studied under various contexts such as logical reasoning, commonsense reason- ing, mathematical reasoning, and theorem proving. We summarize some key works in two sub-areas, most closely related to our work: mathematical reasoning and Science QA.
We also explore Self-Critique (Madaan et al.. 2023; Shinn et al., 2023), where LLM (verifier) is instructed to improve the outputs of the same LLM (generator). We find that this approach is not helpful on JEEBENCH. The verifier is weak in spotting conceptual errors, and like the generator, is itself prone to hallucinations. It would be inter- esting to explore the class of problems where this approach of self-refinement is (not) helpful.
Mathematical problem solving: GSM8K (Cobbe et al., 2021), Dolphin18K (Huang et al., 2016), AQuA-RAT (Ling et al., 2017), MATH (Hendrycks et al., 2021b) and Ape210K (Zhao et al., 2020) are datasets that contain mathematical reasoning questions. Dolphin18K, GSM8K, and AQuA-RAT consist of elementary problems, requiring only ba- sic arithmetic and problem comprehension. Thus, there is a general lack of variety in the underlying reasoning steps across problems, In terms of dif- ficulty, MATH, containing problems from AMC, AIME and Olympiads, comes close to JEEBENCH in terms of complexity. However, compared to MATH, the mathematics questions in our dataset span many additional topics such as Differential and Integral Calculus, Differential Equations, 3D geometry, and Conic Sections. Also, the problems in JEEBENCH are harder, as we discuss later in the paper. miniF2F (Zheng et al., 2022) consists of mathematics problems from MATH dataset and
We further conduct a critical analysis of the lim- its of GPT-4's reasoning abilities, and highlight major areas that require considcrable improvement. A detailed error analysis suggests that it frequently struggles in retrieving relevant concepts required to solve problems, and performing algebraic ma- nipulation & arithmetic. Inability to perform even simple algebra highlights an important question: can we build LLMs faithful to mathematical logic?
Another important question is how to estimate GPT-4's performance in comparison to humans.

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from the year 2016 to 2023. The problems are harvested from publicly available sources." The exam consists of 2 papers held every year, each con- taining 50-60 questions equally distributed among Physics, Chemistry, and Mathematics. We use on- line tools to extract problems from PDF-format exam papers into LЯEX We remove all prob- lems containing diagrams in their description (ap- proximately 40%). Manual quality checks are performed to fix/eliminate possible errors pre- processing. Figure shows representative prob- lems from the final dataset. The problems are cate- gorised by subject: Physics, Chemistry and Mathe- matics, and the format of expected response: multi- ple choice questions (MCQ) with single option cor- rect, MCQs with multiple options correct, Integer- type and Numeric-ty type. In Integer-type questions, the answer is an unbounded non-negative integer, whereas for Numeric-ty ttye., the answer float- ing point number upto digits after the decimal point. The breakdown of the problems based on answer-type and subject shown in Table 1.2
JEEBench
MMLU
MATH
100
25
Motterroaes
Physis
Chemsty
Figure 2: Performance of GPT-4 on random subset of MATH, MMLU-Physics, Chemistry, Math, JEEBENCH
other sources in a formal language. In contrast, problems our dataset are in natural language. General Science: the context of Physics and Chemistry, ScienceQA (Lu et al., 2022), SciQ (Welbl et al., 2017) and MMLU (Hendrycks et al.. 2021a) are prominent available datasets. Sci- enceQA and SciQ, built from elementary and high school science curricula, mainly test factual knowl- edge of the subject. The skills required to solve such problems are primarily information extraction, reading comprehension and commonsense reason- ing. In contrast, questions our datasct require long-horizon reasoning and grounding of com- plex scientific concepts into equations and arith- metic. Concurrently, C-Eval (Huang et al., 2023) and SciBench (Wang et al., 2023a) datasets along similar lines. C-Eval consists of variety of disciplines such as engineering, medicine and humanities has been created. SciBench creates dataset from college-level Mathematics, Physics and Chemistry questions.
Math
Phys
Chem
Single-Correct
53
27
30
110
Multi-Correct
85
41
60
186
Integer
37
22
23
82
Numeric
61
33
43
137
Total
236
123
156
515
Table I: # questions for each subject and problem-type.
The questions contained in the dataset belong to diverse sub-topies (for example, Math questions could belong to Calculus, Algebra, Combinatories, etc.). The breakdown of the entire dataset into sub-topics can be found in Appendix A.I.
Problems present JEEBENCH are signifi- cantly harder than those in other contemporary datasets. To verify this, we sample 50 questions each from JEEBENCH and the test sets of MATH and the high-school Physics, Chemistry and Math- ematics sections from MMLU and conduct zero- shot evaluations on GPT-4. The results can be seen in Figure 2. As we can see, GPT-4 can casily solve more than 80% problems from MMLU. The MATH dataset is harder, where the performance is approximately 60%. However, GPT-4 struggles in JEEBENCH-Math solving close to mere 20% problems.
Experimental Setup and Results
We wish to investigate the following research prob- lems:
1. How well do LLMs perform on JEEBENCH?
2. How effective are methods, such as chain- of-thought prompting and self-consistency, which have been proposed to improve the rea- soning abilities of LLMs?
3.
What are the main sources of errors which limit the performance of these models?
3 The JEEBENCH Dataset
'huts://jeeadv.ae wwt.:aii.nccc.........
The dataset consists of 515 problems extracted from the past editions of the JEE-Advanced
"There are fewer problems in Physics and Chemistry he- cause more problems in these two subjects contained images.

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A eaclosed a cylader with momble Eictioales peston. Its initial ther- mocdgaamic state preseure Pt
GPT series of models text-davinci-003 (GPT- 3), 3pt-3.5-turbo (GPT-3.5) and gpt-4-0314 (GPT-4) as well as text-bison-001 (PaLM-2)
10 changes a final state at ry = (1/30) 10r Pa and V2 = 5x 10-2 of an adlabatic quaci statie process, such that pays = constait Concidee another Esermody namic process that brings the system from the some mitial state to the same stoss bebbaric expersion
valume
folkored by bochorie volumetrie) procese at volame Vy. The amoun, of heot expliidd to the system in the two step procees asprorimately
provided by Google. Evaluation on larger open- sourced LLMs is left for future work.
(A) 112
For obtaining the model's response, each model is prompted with the expected response type con- catenated with the problem description. The exact system and user prompts can be found in the Ap- pendix A.3. The exact answer is extracted man- ually from the response generated by the LLM. Sometimes, the LLM response is gibberish and sometimes responds by saying that none of the op- tions are correct. For both of these cases, we record "None" as the answer. If the question's expected response type doesn't match the question type (for example non-integer for an integer type question), even then we record it as a "None" response.
(C) 588
(T) 8
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Let z- =ledi where - VT, and r.s (1,2.3]. Let P-- and 2 be the ideazity motrix order 2 Then what the total number ordered pairs (r.s) which
(-di"
Figure 3: Instances from the dataset from each subject: Physies (Top). Chemistry (Middle), Math (Bottom)
4. Can LLMs be used to verify their own genera- tions in the context of JEEBENCH? What are the limitations in this behaviour?
We also conduct a few-shot evaluation with ex- amples drawn from 2014 edition of the exam. One example is chosen for each question type and sub- ject pair.
5. How would they perform in an exam setting, where each question could potentially give negative marks when answered incorrectly?
All the proprietary models were prompted be- tween May 17, 2023 and June 23, 2023. The maxi- mum response length is set to 2048 and decoding temperature is set to O. Table 2 contains the results obtained on various LLMs aggregated by subject and question type.
4.1 Metrics
For Single-Coreett MCQs and Integer-type ques- tions, we use accuracy as the metric, that is, a score of 1 if the model response matches the gold re- sponse, otherwise O. For Numeric-type questions, we award a score of if the model response differs from the gold response by atmost 0.01. For Multi- Correct MCQs, we award a score of 1 if the model response matches all the correct options. If any of the options selected by the model is incorrect, we award 0. If the model selects some of the cor- rect options and no incorrect option, then for each correct option in the output, the model is given a score of 0.25. For example, the gold response is ABD and the output response is BD, a score of 0.5 is awarded. This is done, so as to reflect the actual scoring method of JEE-Advanced, which incentivizes student to not guess.?
We observe that open-source models perform as good as random and are, in general, lagging behind proprictary models. Perfor- mance on JEEBENCH increases consistently with newer versions of the GPT model. GPT-3 exhibits near random performance, but GPT-3.5 and GPT-4 perform significantly better. GPT-4 far superior to GPT-3.5, by a large margin of 12.9 points but overall performance still remains close to 30%. It is evident that the performance boost is the high- est for Chemistry, followed by Physics, and lastly Maths. This is probably because the complexity of reasoning is highest in Mathematies questions and least in Chemistry in JEEBENCH. These results highlight the difficulty of the benchmark posed to both open-source and proprietary models.
General trends:
4.2 Prompting LLMs
We evaluate the proposed henchmark on some open-source models: Falson7B-Instruct (AI- mazrouei et al., 2023) and Alpaca-LoRA, which uses low-rank adapation (Hu et al., 2021) to re- produce Alpaca (Taori et al., 2023). Then, we evaluate proprietary models such as the OpenAl's
Hereafter, we focus on just GPT-4's performance since it is far superior to other models. Firstly, we evaluate the performance of methods like zero- shot chain-of-thought prompting (Kojima et al., 2022), self-consistency (Wang et al., 2023b) & self- refinement (Madaan et al., 2023) on JEEBENCH. Zero shot Chain-of- -hain-of-Thought prompting: The
'JEE Advanced also employs negutive marking. We incor- porate it in Section 4.5 while comparing with human perfor- mance in exam setting.

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Chemistry
Mathematics
Physies
Integer
Single-Correct Multi-Correct
Numeric
Total
Random
0.108
0.105
0.103
0.000
0.250
0,144
0.000
0.105
Alpaca-LoRA
0.072
0.101
0.087
0.037
0.164
0.122
0.015
0.089
Falcon7B-Instrucc
0.083
0.114
0.085
0.000
0.182
0.142
0.029
0.098
GPT-3
0.135
0.107
0.134
0.049
0.291
0.133
0.015
0.122
PaLM2
0.192
0.130
0.146
0.073
0.291
0.165
0,073
0.153
GPT-3.5
0.228
0.146
0.173
0,073
0.318
0.249
0.029
0.177
GPT-4
0.423
0,212
0.352
0.207
0.455
0,383
0.153
0,309
GPI-4+CoT
0.468
0,280
0.335
0,256
0.473
0.448
0.175
0.350
GPT-4+ (1-shot) CoT
0.409
0.198
0.323
0.244
0.391
0.340
0.175
0.292
CoT-el Critique
0.487
0,234
0.352
0,280
0.355
0.444
0.219
0.339
GPT-4+CoI+SC05
0.463
0.308
0.449
0.293
0.618
0.410
0.234
0.389
Table 2: This table shows the score obtained by various open-source and proprietary models on JEEBENCH aggregated by subject on the left question type on the right. The overall aggregate scores are in the last column. Note that CoT in the table refers to zero-shot CoT except for GPT-4 + +004+++---0000 (I-shot) COT.
original prompt is concatenated with the phrase Let's think step by step, as proposed by Kojima et al. (2022). We observe that this approach Icads to significant improvement in performance, improv- ing vanilla GPT-4 by 4.2 points.
Now, let's make subetitution to simplity the ogaatoo further. Let Then, vI pt. the equation becomes: vily 4 v/ of -2 4pp
Now, let's square both sides of the equation to etauinate the squize root: ay" -2/3p//
g² 1 ya 4 reorrange 2/Sby/l TT g Sty
and squaze sodes again:
We have the differential opuation
Few-shot Chain-of- Thoughh prompting: We prepend the question with one few-shot example for each question-type, subject pair. Overall, 1-
This find--nder non-linaar differential expaation. Unfortunately. such equations. particular solution that pawes through the point (1.3).
general
we can try
Shot CoT achieves a score of 0.296 as opposed to zero-shot CoT at 0.350 and vanilla GPT-4 at 0.308. Our hypothesis is that few-shot prompting is not very helpful in these questions, because conceptual errors are hard improve upon using few-shot ex- amples. Additionally, many novel reasoning paths might not be covered in the few-shot examples, Thus, our dataset acts as an interesting testbed for advanced approaches in few-shot prompting. Sim- ilar results where scores are better with zero-shot CoT as compared to few-shot CoT have been found in Wang et al. (2023a).
First, ue used to determine the number of valmnce electrons for each central in the compounds.
BeF; Bromine has valence electrons. (B) CIFy: Chácetne (CI) electrous. (C)
(Xe)
elections. (D)
SF.: Sulfur (S) has 6 valence electrons.
Next. we need to determine the number of bonding electrons and non- bonding electroms (Joae pains) for each central stom.
using electrons, valence electrons. forms bonds with 5 possible.
Brig::
electrons left,
Therdore,, this compound not have two lone pains on the central atom.
Figure The figure shows different types error made by GPT-4 its response. (top) exhibits computation error, where the squaring operation performed algebraically wrong. (ii) (middle) response shows a conceptual error where the model is unable to retrieve the relevant concepts required to solve the problem (iii) (bottom) response a grounding error, where the concept correct, however the application terms computing # lone pair electrons on Br BrF's is wrong.
Function calling: Since GPT-4 makes a lot of arithmetic errors, we decide to also test the function-calling API exposed by OpenAI. Since the JEE exam only allows access to basic calcu- lator with 4 primitive arithmetic operation(t, /,/, *), allowing plugins such Wolfram python would not make a fair comparison (for example, directly asking Wolfram integrate function in- stead of doing it from first principles). Instead, to ensure a level-playing field, we use the newly intro- duced function function-calling API to implement standard arithmetic operators.
performance of 0.303 as compared to 0.350 with
gpt-4-0314.
Using a calculator API reduces performance even more to 0.274 from 0.303. We observe that tool usage isn't very robust for GPT-4, where it sometimes hallucinates invalid function arguments and sometimes even invalid function names! Also, GPT-4 is quite accurate at arithmetic for small digit operations. Computation errors are mostly during symbolic manipulation, rather than purely arith- metic operations, which is probably why black- box calculator isn't very beneficial.
Note that function-calling is only allowed with gpt-4-0613 whereas the rest of our results are with gpt-4-0314. We first evaluate gpt-4-0613 with zero- shot COT. Surprisingly, results suggest that gpt- 4-0613 (the new version) is weaker with CoT
Self-Consistency (SC): We sample multiple re- sponses from the LLM at a non-zero temperature.

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For Integer-type, Numericttype and Single-Correct MCQs, we use a majority vote (from all the re- sponses which are not "None") as the proposed answer. For Multi-Correct MCQs, we choose a simplifying assumption that all options are inde- pendent. If an option occurs atleast 50% times in the responses, we select it, otherwise we don't. tt We use r = 0.5 and the number of responses is set to 8. Self-consistency helps lot in improving over the GPT-4+CoT baseline by a score of +3.9 points. In the future, it will be interesting to apply extensions such as adaptive consistency for better cost-quality tradeoff's (Aggarwal et al., 2023).
where the answer is correct (out of 27), the explana- tion also correct. i... 28% of the time, the model gives a correct answer for the wrong reasons.
4.4 Can GPT-4 find and correct its mistakes?
Can GPT-4 be used to grade its own outputs? A good grader should be able to spot errors a solu- tion. Using an LLM to critique its own output has been proposed recently by multiple works (Shinn et al., 2023; Madaan et al., 2023) and has shown improvements
some datasets. A good verifier should be able to catch and fix all errors. Even when the final answer is correct, isn't necessary that intermediate reasoning steps are correct.
4.3 Error Analysis of System Responses
We put the idea of self-critique to test on JEEBENCH. After a CoTT' response has been gen- erated, we prompt another GPT-4 instance by first describing the problem, GPT's solution and then appending the instruction: "Find problems(if any) with the given solutions. If there are any errors, correct it and give the new answer."
In order assess GPT-4's weaknesses, conduct a manual inspection of the errors it makes in its reasoning chains. We perform this study on the errors made by GFT-4+CoT on a random subset of 100 problems. The score obtained on this subset is 27.25. We ask the following questions about the model response for each problem instance:
We re-evaluate the new answer suggested by GPT-4. Results clearly show that this approach doesn't 't lead to improvement. In fact, it leads to poorer results as compared to GPT-4+CoT and the performance goes down from 35% to 33.9%.
1. Is GPT-4 able to retrieve the concepts/facts required for solving the problem? Inability to do this contributes to conceptual errors.
In order to develop deeper understanding of the repairs suggested by the verifier GPT-4, manual inspection is performed. We use the same subset of 100 problems picked up earlier for categoriz- ing error-types. For each generated solution and suggested edit, we pose the following questions:
2. If relevant concepts are retrieved, are they grounded correctly as equations/constrainns? These contribute to grounding errors.
3. Is the algebraic manipulation & arithmetic cor- rect? These contribute to computation errors.
Refer to Figure 4 for an illustration of each type of error In one case, we find that GPT-4 misun- derstands the question. The overall results of this analysis shown in Table 3.
Can the verifier find problems in the solution?
Can the verifier fix problems if it finds them?
the problem identified by the verifier actu- ally a valid problem?
Error Type Conceptual Error
Count
34
Error in solution?
Verifier Response
Count
Computation Error
30
Yes
No error found
46
Grounding Error
15
Found crror but didn't fix
25
Problem Miscomprehension
Converted non-error
Perfect
20
Found error and fixed
No
Converted non-error error
Table 3:
Variety of errors GPT-4 makes in the solution.
Didn't find
Table 4: The figure shows the breakup of the kind of errors the verifier GPT-4 makes while suggesting edits.
Our error analysis indicates that most errors are caused because of not being able to retrieve con- cepts (34 out of 80), which are critical to making progress in the solution, or due to computation errors (30 out of 80). Moreover, in 20 questions,
Our results can be seen in Table 4. It is evident that, contrary to observations in other works, on JEEBENCH, GPT-4 is mostly (16 57.5%) un- able to find errors in solutions it proposes. Even when it can, it is unable to fix them. Only in 2
'Note that a question can have multiple types of crrors, however, we only investigate the first error that is noticed.

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Quesstion
fidence levels. Contingent on the former skill, the latter is a simple thhision- computation under uncertainty.
Let S he the act of all complex numbers sntadlying [2 4 i| 1. Then which of the bollwwnng statemesss syfore TRUE?
(A) la S for
(B) bl S for s
(C) [++ 2 for all e S
(D) The sot S his evartyy four dlements
Antewer
4.5.1 Deciding whether to answer
First, we can rewrite given eqpaation follows an [22
1, +
To attain good score in the examination, it is im- portant ensure that the model does not answer when it is unsure of its solution. Can LLMs as- sess this risk and plan accordingly when prompted with the marking scheme? To investigate this, we prompt the model with the exact marking scheme for each MCQ question type along with problem statement, and then ask to generate an answer or skip the question altogether. The complete prompt is in Appendix A.6. We re-run inference all the problems with these prompts for all the MCQ questions. The results can be seen in Table 5.
There ertur gramn solution. equation 1=++ incoreetty derived from the original equation (s + 2-11 should bet
1| 1. The correct derivation
=1
Question
A metal is boated in a furnace wheee sensor hept above metal surface to
read the power cadiated (P) by the metal. The sensor has scale that dieplags
lott (P)P)), where Py constant. When the metal warface of 187 C, setsor shows a value Assame surfoce remaine constant. What is the value displayed by the secsce when the temperature of the metal surince is raised to 2T07"C ?
temperature
emmiscivity the metallic
Step Calculate
to plug the
2i and and calculate the final unewer: ket, 22 11600111
1gt/22 16*)=
The given solution is conrect, and the fixal ouswer 17. The steps are well. explalaed, and the oskulations are ocetmate.
Figure 5: [Top]: Question where GPT-4 identifies mistake but is unable to fix it. The problem and part of the response is on the top. The bottom block contains the edit suggested by GPT-4. The manipulation in the edit suggested is mathe- matically wrong. [Bottom]: Question where GPT-4 is unable to identify error. The problem and part of the response is the top. The bottom block contains the edit suggested by GPT-4. It should be (2 4') instead of (2 (2)16*)
Method
Pos. Score
Neg. Score
Total
GPT-4+CoT wio Marking
489
181
308
GPT-4+Col Marking
404
206
198
Table 5: Marks obtained when GPT-4 prompted with the marking scheme v/s without on MCQ questions. These marks are out of a total of 1074.
Results indicate that prompting is not helpful in this case, and GPT-4 cannot effectively decide when not to answer. This is in line with observa- tions made by Valmeekam et al. (2022) where it is shown that LLMs have poor planning capabili- ties. In response, we develop a post-hoc confidence- thresholding method on self-consistency responses.
out of 80 questions, meaningful edit to an erroneous solution, which is over-compensated by the solutions degrades by suggesting edits to parts of the solution which are already correct. Figures 5 provides example of errors made by the verifier. The complete response for these along with other examples can be found in the Appendix A.5. This experiment raises an interesting question: for what class of problems is self-critique (not) helpful? It might be interest- ing look into methods which use learnt verifiers (Arora and Kambhampati, 2023; Lightman et al., 2023).
GPT-4 able to give
4.5.2 Calibration
For single-correct & multiple-correct MCQs, we compute the confidence score for each option by computing its relative frequency in the set of re- sponses. Note that often, GPT-4 is unable to answer the question at all, or arrives at conclusion that is not supported by any option (a "Nonc" response). In such cases, we do not count contributions from this response. For instance, if a model's response in 4 attempts in a Multi-Correct MCQ is "AB", "Nonc", "B", "AC", then, the confidence for op- tions are A: B: C:J. D:O.
4.5 Comparison with human performance
The JEE exam contains negative marking, for in- stance, single-correct MCQ questions are awarded a score of +3 marks when correct, -1 if answered incorrectly and O when not answered. For Mult- Correct MCQs, score of +4 is given when all options are included in the final answer. If any of the options is wrong, -2 is given. If some of the options are correct, +1 is given for each correct option. The skills needed for an examinee to maxi- mize one's score include being able to assess one's own confidence in their response and being able to decide whether to answer or not based on the con-
Figure 6 is the calibration cruve of GPT-4 on JEEBENCH. The maximum calibration Error (MCE is 0. and the average calibration error (ACE)$ is 0.098. The plot suggests that the model is slightly overconfident at high confidences, because of lower accuracy on higher levels of confidence,
MCF/ACF are the maximum/average absolute difference h/w confidence & accuracy among all confidence bins.

---

60
Sh-------hhrehhlddm (PT4-00T+5
50
Norralized
GFT4-Co1]
3201P4C)
GCT3
0
Falcop-78
100
GPT3.5
10% Humnn Performanc
Alpaca
Performance
Corfitonce
-20
Model
Figure 6: Calibration plot of GPT-4 on MCQ questions
Figure 8: Plot showing performance of models com- pared to projected human performance.
Soaee
120
best strategy for single-correct MCQs. However, this not true for multi multi-correct MCQs, where a threshold of Timaitiple = 0.5 (as done originally) is sub-optimal. We assume that Integer and Numeric questions do not have negative marking. The final response for them is decided using majority vote responses. Table shows scores with the optimal thresholds on the test set. We find that not answering when confidence is less than threshold increases the total score by about 4.3%.
0.2
5/1
0.6
200
PLB.
5.5
FLB
Method GPT-4+CoT
Pos Score
Neg Score
Total Score
Figure 7: Scores obtained on different thresholding val- ues on Single-Correcttop) and )otti)))rrec(bbottomm) type questions from the val set, the optimal value is Tsingle 0.125 and Tmultiple = 0.75
109
43
66
GPT-4+GNTWSC
118
49
69
GPT-4-CoT-SC-Thhcallddg
111
39
72
Table 6: Marks the test set obtained when optimal thresh- olds derived from the val set used.
but slightly underconfident at low and medium con- fidences.
4.6
Estimating performance compared to humans
4.5.3 Thresholding with Self-Consistency
Finally, we wish to estimate the performance GPT-4 compared to humans. For this we use the 2023 examination paper since there is almost no probability of contamination. The 2023 paper was released on 4th June 2023 and contained 65 ques- tions which
Our objective is to decide whether to include an option in the final response or not. We wish to com- pute parameter such that an option will be in the final response if the confidence for that option is atleast We compute separate Tsingles Tradttipee for Single-correct and Multiplecoorrect MCQs re- spectively. We compute confidence scores for GPT-4's response to each question as in Section 4.5.2. Questions from 2016-2021 are chosen as the validation set and from 2022-2023 as the test set. The best Tsingle: and Traudtiple thresholds for Single-Correct and Multi-Correct MCQs by simple dyper-parameter search. Figure 7 shows the plot of positive, negative and total score the vali- dation set over range of possible values of Teingle and Tmaltiple- The optimal value of Tmuutiple 0.75 and of Tiingle is 0.125. Tsingle being less than 0.25 indicates that taking a majority vote is the
textual (rest 37 contained images). The total marks in the exam were 360. The score obtained by GPT-4 after mmnfidence-thresholdm on MCQs and regular aggregation on Integer and Numeric types is 49 out of 229. Assuming average difficulty levels in questions that were not included (because they contained images) is equal to ones that were, we normalize the projected human per- formance from 229 to 360 giving it a total of 77 marks out of 360. Projections indicates that this would place GPT-4 around the 80-90 percentile range. The results of JEE Advanced 2023 indicate that the top 10% score is approximately 97/360 and top 20% score is approximately 70/360. Figure 8

---

provides comparison of the performance of LLMs along with human performance of the applicants.
the multi-modal version of GPT-4, evaluating this aspect of Physies problems might be casier.
Finally, an LLM that understands its own con- fidence in an answer is a key missing piece, highlighted by experiments in the exam setting. Our simple post-hoe wrapper does slightly improve performance in this regard.
4.7 Has GPT-4 memorized some problems?
the era of internet-scale pre-training, very hard to ascertain whether a dataset has been used for training a particular model. Neverthe- less, we tried to investigate the contamination of JEEBENCH. This was done by (i) searching for instances in JEEBENCH from publicly available internet corpora, (ii) prompting the LLM to com- plete the problem statement itself, when prompted with prefix of the problem statement. Both these investigations suggest only minor (approx. 6%) contamination. detailed description of our con- tamination study can be found in Appendix A.8.
6 Conclusion
We present a challenging problem solving bench- mark to evaluate large language models. We per- form detailed analysis of the performance of var- ious LLMs on the benchmark, and identify areas of improvement in the best current LLMs. Our work raises interesting research directions such mathematical logic-augmented GPT, multi-modal evaluations on GPT-4 and the decision-makiin ca- pabilites of GPT in an exam setting. We hope that JEEBENCH guides future research in reasoning us- ing LLMs. Our code and dataset are available at https: t/tts://github..o wwwoom/aarriittee jeebench.
GPT-4+CoT+SC attains score of 0.338 on Ad- vanced 2023 questions, which is not very far from the aggregate performance of 0.396 on the remain- ing dataset. Given that 2023 questions are uncon- taminated, we believe that the extent contam- ination is quite low, and its performance on this dataset is genuine indication of its current reason- ing abilities. It is also noteworthy that some exams (e.g., JEE Advanced 2017) are easier, and GPT-4 does much better on it raising the aggregate score.
Acknowledgements
We thank Dr. Parag Singla and the JEE Office for helping with getting approvals use the dataset for research purposes. We thank Rishabh Ran- jan for discussions around calibration and Mohd. Zaki for help in obtaining OpenAl API access for this work. The work supported by grants from Google, Verisk, Microsoft, and Huawei, and the Jai Gupta chair fellowship by IIT Delhi. We thank the IIT-D HPC facility for its computational resources.
5 Discussion
The general performance trend demonstrates the ef- ficacy of high-quality data, instruction fine-tuning:
RLHF and parameter scaling in improving the rea- soning capabilities of LLMs. For many problems, GPT-4 is able give sketch of the correct, human- like solution that impressive given the extent of reasoning involved in the problems. However, our analysis also reveals major areas where progress needed. Although GPT-4 performs flawless logi- cal and mathematical reasoning in some instances, sometimes it commits grave errors in trivial steps. Errors in retrieval and application of concepts suggest an interesting research question: Can we augment an LLM such that it's generation is con- strained by faithfulness to a set of facts? Such a system will demonstrate robustness in reasoning, critical for long-horizon tasks.
Limitations
Contamination is big problem in the era of pre- trained language models which have been trained on large web corpora. Therefore, its really hard to determine if a dataset has been seen. Determin- ing the extent of contamination is also not easy, although we make an effort to quantify it. Eval- uations against humans is also a slightly flawed process due to other limitations such as time pres- sure during the examination procedure. Addition- ally. this data's distribution is fixed to pre-college Physics, Chemistry and Mathematics. There are more gradations and difficulty levels at which the model can be evaluated which have not been tested as part of our analysis.
Physics problems in the benchmark often require an understanding of spatial reasoning. We found that while GPT-4's spatial reasoning is far from perfect. Appendix A.7 provides an example where GPT-4 commits errors which might be attributed to its inability to reason spatially. With the release of

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A Appendix
'hee reaction force of the wall on the stick is per- pendieular the stick. The stick makes an angle of 30 with the wall and the bottom of the stick on a rough floor. The reaction of the wall on the stick is equal in magnitude to the reaction of the floor on the stick. The ratio h//l and the frictional force f at the bottom of the stick are (g 10 ms s²)
A.1
Sub-topic wise distribution of questions in JEEBENCH
Figure 9 provides the topic wise distribution of problems in JEEBENCHfor each subject.
A.2 Example problems from JEEBENCH
(A) 4
v3
16 f
16y3
N
Here we present few problems from
JEEBENCHalong with expert written solu- tions, with concepts being highlighted in yellow, their grounding being highlighted in violet, and the final algebraic manipulation highlighted green. See Figures for a Math Problem, Figure 11 for a Physics problem and Figure 12 for Chemistry problem.
(B) [ I
16y3
3
N
(C) 3v3
8v3
16
3
(D) 16
3v3
16y3 N
A.4 Errors made by GPT-4
Here we present some errors made by GPT-4.
A.3 Exact Prompts for GPT models
A.4.1 Conceptual Error
For each problem, we prompt the model with the required answer type and then the prompt. For different response types, the prompts are:
This is example of Conceptual Error
What is the total number of distinet x E [0,,
for which for I+, = 2x 1?
GPT-4 Response
1. Single-Correct: In this problem, only option will be correct. Give detailed solution and end the solution with the final answer.
First, we can differentiate both sides of the equation with respect to get rid of the integral. Using the Fundamental Theorem of Calculus, we have: dx (Jo x² T+2 2 This is an error because two func- tions are equal at point, that doesn't imply that their derivatives are also equal.
1+11 (+adit
(2
(2x- 12x-11 1)
2. Multi-Correct: In this problem, multiple options
be correct. Give a detailed solution and end the solution with the final answer.
Now, want to find the number of distinct solutions for in the interval [o, 1]. Let's first solve the equation for x: x²2 2(1 x4)
3. Integer: In this problem, the final answer will be a non-negative integer. Give a detailed solution and end the solution with the final answer.
x² - 2r 2
This quartic equation, and we can try find its roots. Notice that x 0 is not solution, so we can divide both sides by x::
4. Numeric: In this problem, the final will be a numeric value. Give the numerical answer correct upto the 2nd decimal digit. Give a detailed solution and end the solution with the final answer.
2x -2x -x 0
Now, let y 2 Then, have:
2y 2y 1=
2y²² - This a quadratic equation in y. We can use the quadratic formula to find its roots:
The entire prompt consists of the question prepended with the response type prompt. An ex- ample is:
y -(-)) (-1))(11)1--200011 4(2)(1)
In this problem, only one option will be correct.
2(2)
y 1=v1-8
Give detailed solution and end the solution with the final answer.
Since the discriminant is negative (1 - 8 = -7), there are no real roots for Therefore, there are no real solutions for x in the given equation.
Problem: A uniform wooden stick of mass 1.6 kg and length rests in an inclined manner on a smooth, vertical wall of height f(<< 1) such that a small portion of the stick extends beyond the wall.
Thus, the total number of distinct [0, for
which fo" 1 dt = 2x 1 is 0

---

Physics
Chemistry
Mathematics
33.3%
29
46.9% 5%
17.9%
4774
25.39 35
22 9%
2.
Necha lioe a Modem Physio Eedbonagnitton "Thrrol Piyeiie a Wovee Ancustie a Opttos mrmm and Masounemerts
hargan Chembtry Stten Materr Cochority Chemiiaa Equlitees Digunk Cheniay
Calculie Mgehra Cancliat Geomaty Combonetiics Pistwellly Tigenoneity
Figure 9: Topic-wise distribution of problems in our dataset
f: (0,)0) - differentishle fonction such (((x) -2
the depth well by messuring the time interval impact the E2
lot all = (0, oc) and [(3) #11 Thes
receiving mouurement mossures the depch of the well be 20 meters, Take acceleaaton ma-2 and the velucity of sound 800 me Than the fractional crror in the messurement, 6L/2, is cloast
droppinng
(A) lim--o- ( -1
well. eroor
va =f
k0 (2)
duee tn guaviyy giavrly
(D) [[(]] S
(0.2)
[A] 0:25%
Solution
[8] 1%6
we are given that (()) Mel for (0,oo). this fins order linear differential equation:
[D) 6%
Sulution
((e) + Mec
The thms taken to bear the sound of impart would be the time taken for the stote noch the botsom of the well in vuditiion to the time talen the the
To this differential equation, can integrating factor. integrating factor given
x. Multiplying both sides the
that fart, (Br. The taken for the wound mecch top the & wheme is the velocity of sound. Therdorn.
Stoce the stone deopped from
differential equation by the integrating faclor, we
ax [())
Now, notice that the left-hand side the dectvative of the product sf(s);
Since we need to find the fractional error the measurement of Ly that is 4. differentiate both side of the equation with respect Using this, got
(xf(xx))
Integrating sides respect un gel:
2
=f(x) m x=+++ C.C.
2V
Lg
where the
Integration.
To find value C,
6T
given condition /(1) Plugsing
we get f(1) C.
A1.
Since f(1) y 1. we umst have C Now, we can write the funetion ((x) as f(x) = + E. and (((s) = 1-
iv
For option A. ((())
Cx., therefore lim..ns ('(A)
61.
6T
ST
option af(3) a Cx) Cx", theredore lo, xf(?)
/2,
!)
2
For option C, (x) x² C, therefore hm.-o- (()) since
0.01
0.01 18
15
1%
arbitzuy value, f(e) unbounded
20
23.
().
Therefor,, the unswer
Figure 11: A Physics problem
Figure 10: A Math problem
First, let's consider the refraction of light through the lens. Since the image formed is double the size of the object, we can say that the image distance (v) twice the object distance (u). So, 2u 60 em. This is an example of wrong grounding. The equation generated inconsistent with the sign convention which must be taken care of for the other equations to be valid.
A.4.2 Grounding Error
This is an example of a Grounding Error A plano-convex lens is made of material of refractive index n. When a small object is placed 30 em away in front of the curved surface of the lens, image of double the size of the object is produced. Due to reflection from the convex surface of the lens, another faint image is observed a distance of 10 em away from the lens, Which of the following statement(s) is(are) true?
Now, let's use the lens maker's formula to find the focal length (f) of the lens: } = (n
1) (1-
(A) The refractive index of the lens is 2.5
(B) The radius of curvature of the convex surface
Since the lens is plano-convex, one surface is flat, and its radius of curvature (R₁ is infinite. The radius of curvature of the convex surface (R2) is negative because it convex. So, the formula
is 45
(C) The faint image erect and real
(D) The focal length of the lens is 20 cm
becomes: } (n (11 ()(0- -kk) (n 1) 1)A,
GPT-4 Response

---

Galena one) partially
passing through high
real.
perature. After some time, the pazeage air stogped, but the beating contimmed in a closed furnace such that the contents undergo self reduction. What weight
of produced per kg Oş consumed? (Asomic weights gmol 1 16, 32, Solution
Galenn(led sullide) when partially odidiond couverts lod oxide which furthes the lead sullide
load metal. The reaction follows:
A.4.3 Computation Error
2P18 + 50g - 2P10 250g
PtS 2Pb0 33% SOs
The net reaction can be written as
Here is example of computation error.
PbS O2 Pb SOg
Two inductors L1 (inductance 1mH, internal re- sistance 3Ω2 ) and L2 (inductance 2mH, internal resistance 4Ω and a resistor R (resistance 1252 are all connected in parallel across V battery. The circuit is switched on at time = O. What is the ratio of the maximum to the minimum current
Therefore, moles of led metal produced will be equal to moles of oxygen consumed. Therefore, have
weight of oeygen
weight of lead
molar mass of oxygge
molat mass of lead
weight load
32
207
Theredor,, weight produced per kg oxygen 2017 /22 6.47
(Imax/ 4 drawn from the battery?
Figure 12: A Chemistry problem
GPT-4 Response
Step I: Analyze the circuit at t=0 At t=0, the induc- tors behave like short eircuits (since they have not had time to build up any magnetic field). There- fore, the current through each inductor is deter- mined by their internal resistances. Let's call the currents through L1, L2, and R as 41 12, and IR respectively. Using Ohm's 's law, we can find these currents:
Now, lct's use the lens formula to relate u, v, and
f: 1
+ 8
Substituting the values of u and u: }
1
30
60
60
20
Now, we can substitute the expression for from
the lens maker's formula: (n 1) Rs = 20
Now, let's consider the reflection of light from the convex surface of the lens. Since the faint image is formed at a distance of 10 cm from the lens, we can use the mirror formula to relate the object distance (u), image distance (v'), and radius of curvature (R2) for the convex surface: 1 1+ Rg a
11 = 30
5V
3
12 = 5V
3 A
IR
5V
120
The total current drawn from the battery at t=0 is the sum of these currents:
Substituting the values of and v':
6
Now, we can solve for R2: Rg = = 12 em
Imxx 11 12+
1
+
12
5
20
12
Wrong calculation, should be
40
Now, we can substitute the value of R2 back into
12
the equation relating n and Ry: (n - 1) 1 20 Solving for n +
Step 2: Analyze the circuit at steady state As time goes to infinity, the inductors will behave like open circuits (since their magnetic fields have reached their maximum and no more current can flow through them). In this case, the only current flowing in the circuit is through the resistor R. Us- ing Ohm's 's law, we can find this current:
12 20
= 1+ 3 = 5 1.6
Now, let's analyze the given options: (A) The refractive index of the lens is 2.5 This is false, as we found the refractive index to be 1.6. (B) The radius of curvature of the convex surface is 45 em This is false, we found the radius of curvature be 12 cm.
(C) The faint image is erect and real This is true. Since the faint image is formed due to reflection from the convex surface, it behaves like a convex mirror. For a convex mirror, the image is always erect and virtual.
Imin =
5V 5 120 T2
Step 3: Find the ratio of maximum to minimum current Now we can find the ratio of the maximum to the minimum current:
(D) The focal length of the lens is 20 cm This is which
Imax Tiin
20
false, as we found the focal length be 1 20'
is 0.05 cm.
So the ratio of the maximum to the minimum current drawn from the battery is 4.
Final Answer: (C) The faint image is erect and

---

A.5 GPT-4 verifying its own response Figures 13, and 14 demonstrate various cases which arise when GPT-4 critiques its own output.
Question
I
_t zertyda, then
log,
D 1> 50
Answer
Flass, evaltate the integral for single term in
computerr producing buctory only two plants Ti and Ty. Plant Ti pro- duces 20% and plant To prodiaces 80% of the total computers produoed. 7% of computers produced in the factory turn out to defective. known that P (comsuterr turns out defective given that produced in plant Ti
can perform subetitution:
da du.
The limits of integration bocome - k 1 to u - k++. The integral becomes:
I0P computer turns to defective given that producee plant
Now, we can perform partial fraction decomposition on the integrand:
Tb),
tu- _ +
where P(E) donates the prokadality of an event E. A computer produced in
Multiplying both sides by (v 1)uu clear the denominstors, we get.
the fuctory is randomll selected and does not turn out to be defective. Then
++ 1 - A(s) + Diu 1)
prohebility produced in plant Z.
To solvv for A and Rt we can use the following system of equations:
(A]
Set
H013 1)
2. Set k A(-1)
-(k
So, the integrand becomes:
Tue --- + 0 =(h=1)
Auss wer
Now: we can integraze:
First, let's define eventa: the computer defective N: the computer
fest -le+3)
-A 1u13 _indu
is not defective- - Ty: the compater is produced in plant Ti Ty: the competer
integral
simple natural loparithm::
is produced plant Y3
-Ik + 1] [la 11l--1 -(k 1))0n(k + 1111))aa ln.k)
We
the following, prokaliiltiin: P(T)) 0.2 P(T))
Now, we can substitute this back into the original sum:
P(D) = 0.07 - P(D)T)) = (DT()
I you (-(k 1)/2m(k +1] Ind)]
We want to find the prohabiliny P(Tz)N), which is the probability that computer peoduesd plaut given that is not defective.
Notioe
telescoping wam:
I (((2 ln2 11.1) (3In3 22n +-..+
98 (8]) I = -Inn 1 In2 +-..- 98 + In199) = [n[ 22...- 99) Now, let's
(90 +99.-
We cun use the Bayes' theorem to find this probability:
P(TE]N)= 218g
to ete
-n(1)-2..... 2-....- -99) ln
First, let's find P(N/T)) (i)). We know that P(D[T)) + P(N)2)) - 1, soc
So, I < e which meons option [D] is correct.
P(VIT)) -1 1- P(D)Z))
Now, let's compare 1 to 30:
Now, let's and P(DIT)). We use given information P(D)T))
Since - In[1 2
98 99), know that 7 is negative. Therefore,
10P(D/T)) and the fact that P(D) - P(T) P(D)))))(E))
1.. means option [C] correct.
0.07 - 10E((D)))002)) P(D/))]00))
Thus, the final answer is [B] and (C).
Solving P(D/T)), get:
Gold: BD
Now we can 3 P((T))):
P(D[Z)) =
Edit
The given solution is incoreet.. excor occurs in step where the integral
P(N))) - P(D)[)]
10
Is evalusted:
Next, let's find P(N). We know that P(N) + P(D)=
- du = -(A 1] [In (x 1//49 -(k 1)[lnkk= 1) In
P(V) -1 1- P(D) 11 0.07 0.53
The cottect evaluation should be:
Finally,
find P()]N) using the Bayes' thearem:
(--3 -(hat) du
1)/Bn/u 10t:?
1)(ln( In(k
P(T] A "B
Now, ous subatitute this back into original sum:
So the probabilitt that a computer is produced in plant T3 given that
I - -=t,;-(f + 1)(ln( + 2) In(k + 1)))
not defective is
Notioe that this is telexcoping sum:
The a0"
33-
---1(1nn---- ln2) (8314-2113))
21n3)
100 -8 (91)
Edit
1--(nn2 4- 4-... Ia +1-4-000000 - 1n 3........9 100)
The gives solution crroet, the (C) 90-
Now, let's comgare/ to lne")
In(2 --... 999 100) Inne
So, e" which
option [BB] cornect.
Figure 13: GPT-4 identifying correct solution as cor-
Now, let's compare
6):
Since I - In(2 5 3..... 39 100),, we know that is negative. Therefor,,
rect.
< St which menns option [C] is conrect.
Thas the final asswer
and
Figure 14: GPT-4 identifying correct step as wrong and making it incorrect
A.6 GPT-4 deciding when to attempt
To evaluate if GPT-4 could decide whether to refuse attempt given the negative penalty for being wrong, we prompt it with the following for Single-Correct MCQ questions. If the answer is wrong, you'll 11 be given -1 marks. If the answer is correct, yo be given +3 marks. If you're re unsure of the answer, you can skip the question, and you'll 11 be given marks.
A.7 Can GPT-4 reason spatially
Figures 15 and 16 show an example problem for which GPT-4's response indicates inability to ground physical concepts spatially.
A.8 Is JEEEBENCH contaminated?
We note that several web-pages on the internet do provide problems and solutions to the JEE exam (for example, here). Therefore, it is possible to find the question and the answer during web-scale training. Unfortunately, this would be true for any dataset that has been created from questions from competitive exams such as (Hendrycks al., 2021b).
The following is used for Multi-Correct MCQs If any of the options in the final answer is wrong, yo be given -2 marks. If all the options are correct, you'll be given +4 marks. If some of the options are correct, you'll 11 be given +1 for each correct option. If you're unsure of the answer, you can skip the question, and you'll be given marks.
To check if the dataset is indeed contaminated, we perform a careful analysis along the following

---

A uniform wooden stick of mase 1. liz and longth i rost in an inclized manner smooth, vertical wall of bright M- 1) such that small portion of the stick extends beyond the wall. The
Questionn
farmur land the shape of triangle with wrrliens P00.0 (10.0).01. and RV2, 0). From this ladd, neighbouring farmer Fs takes away the regioo which lins betwaam the side PQ and nurve of the form o m and (n > 1). the by the farmer F2 is exuulyy 20% of the ares LPQR, what the value of
is peppndicshar the stick. The stick makes an angle aur with wall and the bottom of the stick on nuugh florr The resction of the wall on the stick equal in maguitade to the reaction of the floor on the stick. The ratio h/1 and the frictional force/ the bottom of the stick ane
wall
areas the
taken
Auswer
(s 10 sf)
Step I: Find the arsa of triangle PQR.
(A)
1 N N
The urea of triangl with wrthow ot (It, stl (rai and (1%, os) can bu
[B)
found usiing the formula::
.irco = flai(u - uc) + solis - 9i + zy(i) - uell
[C)
Plugcing in the coondinatee of Q. and R. we gos:
(D)
Areerga 1,001 - 0)+ - O) + 2(0 1)] 4 2]
Step Drow diagram of the situationn
Step Find area the region talem owny farmes Fs.
on the side and horisceral on the bottom, Place the wooden stick inelined at an angle of 30" with the wall, with Its bottom end on the floor and its top end touching the unil. Label the point where the stick touches the wall A. the point where the stick touches the floor stick C.
Draw a vertioal
Since the arsa the region taken away by farmer Fa is exactly 50% of the
area of APQR we have:
Arsos, 0.3 Arestgge 0.3 I 0.3
Step Set up an integaal to find
under curve y xx betwees.
and center of mass
P Pand Q
Step Ideutify the forces acting on the stick.
The area under the curve an betwren P and Q can be found using the Iutegral:
There are four forees acting on the stick 1. The govitational force acting C, which equal the moss of the stick x 10 foreo eseeted by the wall on the stick at point A, acting perpendieuhur to the stick. B. The normal force exceted by the floor on the stick at point B. acting perpesdieular
downward the center
Artovare = s'der
times the acederatton due to gavity: my
2. normal
Step I. Calesiltte the integral and see is equal to the area taken away by
fatmer Fs.
C. ="=dd= - GITT ter Grr mi
1
The frictional
by the floor the
Now, we stt, this equal to the area taken away by farmer Fg!
stick at point B, acting parallel to flooe and apposite the direction the stick's e motioon.
2nt -0.3
Step Solve for
Step It Apply the rquillbriumm conditiion..
To find the value of i. we simply solve the equation:
equilibrium, not force and torgue acting it must be zero, This gives us two equationae
Since the stick
in 1 - 1
30
1. The not force in the horisontal direction moro: I m Na 50P, 2. The ne force the vertical direction meve Ny N₂ 30" mg. 3. The net $ my A.
torque about point
Figure 16: Error made by GPT-4 in understanding which curves to take area between. Here it is taking area be- tween the curve and x-axis. However, the question in- tended between curve and the x = y line. This indicates that GPT-4 might be weak in 2D reasoning from purely text-based prompts.
Step 4: Solve for the unknowns.
We are given that the reaction force of the wall the stick equaal in stick, Lo., Na No. Using
magninade to the reaction of the floor Information, we oan solve the above equations for the unknowns h/t and f. From equaatio 2, we have Ng N2 con 30 mg. Since NA No. this on NA 30030 + mg. Solving Na. get NA Too N.
Figure 15: Error made by GPT-4 in understanding phys- ical concepts. In this example, correct form of equation should be/ f = Na cos 30". GPT-4 fails to spatially ground the concept of direction in 2D environment.
minor decrease in overall scores for most models. This suggests that the contamination doesn't impact the scores greatly. For instance, GPT4+CoT performance goes down from 0.350 to 0.347. The average performance on the 30 questions is 0.392 (this could also be attributed to a much higher proportion of chemistry questions which GPT-4 is better at). is noteworthy that even though these URLS are present in Common Crawl, that doesn't 't imply they have been trained on since LLM training doesn't 't even complete epoch over the pretraining data generally.
axes:
1. We search the C4 dataset (Dodge et al., 2021) using 50 randomly sampled questions from Physics and Chemistry(we do not use Math question since they are generally more LaTeX heavy and exact matches would be harder to find). We were unable to find any documents containing sufficiently long substrings from the questions of the dataset.
3. Since proprietary LLMs do not disclose their exact data recipe, we wish to see if GPT- 4 memorized the questions present in the dataset? Note that this is a very challenging problem in itself. Inspired by contemporary methods 6 we take the following heuristic ap- proach: we prompt GPT-4 with a prefix of the question and instruct it to complete the remaining question providing context of the year (for eg, Complete this question from JEE Advanced 2017). In this, we check if the
2. We search the Common Crawl URL Index http://urlsearchh commoncrawl org/ CC-MAIN-2023 2013-2222222323 23/ for several popular sources which release consolidated full length solutions to exams from the JEE exams. Out of 500+ questions in our dataset, we could find web pages containing the solution of 30 questions [19 chemistry, 11 math] in the latest Common Crawl 2023 index. This is less than 6% of the dataset. We study the effects this has on the final evaluation and observe that removing these questions results in only a

---

model is able generate "precarious" data, that is. data which cannot be predicted from the context. For example, some numerical data provided, or the options of the questions. We use the same 50 questions sampled above and prompt GPT-4. Our evaluation suggested GPT-4 was unable to generate any such re- sponses.
These facts suggest that the extent of contamina- tion, if any, is very low and that is not detrimental to the paper. We will add these analyses in the final paper to clear any aspersions regarding possible contamination.
We additionally want to emphasize that our benchmark is dynamic in the sense that a new set 40-50 new uncontaminated problems can be added to it annually. This would allow reliable test of the problem solving abilities of future LLMs more and more data goes into training them.


Reviews:
[{"review_id": "nMeD1Gf04O", "reviewer": "Reviewer_Yhxr", "summary": "", "questions": "1. Could authors provide some justification that the exam & answer they used are not easily accessed in LLM pre-training? (e.g., answer is located in different places from question)\n2. The authors mention the calculation capability of LLM. Could the authors try adding some calculation tools (like python / wolfram alpha) to LLM and see its performance?\n3. The examples are blurred. It would be better to provide clear examples for such a dataset paper.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their insightful comments and suggestions. We appreciate the fact that the reviewer finds our dataset nice and analysis thorough. \n\nThe following points provide clarifications to concerns raised by the reviewer. \n\n**The dataset could be present in LLM pre-training**\n\nFirstly, please note that the dataset requires significant preprocessing for converting into a LaTeX format. This occasionally requires human intervention in some cases due to errors made by automatic tools.  \n\nFurther, we wish to draw the reviewer’s attention to Section 4.7 of our manuscript where we mention that the performance of GPT-4 on JEE’23 (conducted in July 2023, so definitely not in the pre-training data) is sufficiently close to its average performance on the past years indicating minimal (if any) memorization. \n\nWe note that several web-pages on the internet do provide problems and solutions to the JEE exam. Here is an example: https://www.toppr.com/ask/question/highly-excited-states-for-hydrogenlike-atoms-also-called-ryberg-states/. Therefore, it is possible to find the question and the answer during web-scale training. Unfortunately, this would be true for any dataset that has been created from questions from competitive exams (for example MATH, AGIEval). \n\nTo check if the dataset is indeed contaminated, we perform a careful analysis along the following axes:\n\n**Are the solutions present in popular open-source pretraining datasets?**\n\n1. We search the C4 dataset (https://c4-search.apps.allenai.org/) using 50 randomly sampled questions from Physics and Chemistry(we do not use Math question since they are generally more LaTeX heavy and exact matches would be harder to find). We were *unable to find any documents* containing sufficiently long substrings from the questions of the dataset.\n\n2. We search the Common Crawl URL Index (http://urlsearch.commoncrawl.org/) for 6 popular sources which release consolidated full length solutions to exams from the JEE exams: Aakash, Byju’s, FIITJEE, Resonance, Vedantu, and Vidyamandir. Out of 500+ questions in our dataset, we could find web pages containing the solution of 30 questions [19 chemistry, 11 math] in the latest Common Crawl 2023 index. This is less than 6% of the dataset.\nWe study the effects this has on the final evaluation and observe that removing these questions results in only a minor decrease in overall scores for most models. This suggests that the contamination doesn’t impact the scores greatly. For instance, GPT4+CoT performance goes down from 0.350 to 0.347. The average performance on the 30 questions is 0.392(this could also be attributed to a much higher proportion of chemistry questions which GPT-4 is better at). Even though there isn't much difference in the performance on (possibly) contaminated and (possibly) non-contaminated questions, we will remove them from the dataset.\n\nIt is noteworthy that even though these URLs are present in Common Crawl, that doesn’t imply they have been trained on since LLM training doesn’t even complete 1 epoch over the pretraining data generally.\n\n\nSince proprietary LLMs do not disclose their exact data recipe, we wish to see if GPT-4 memorized the questions present in the dataset? Note that this is a very challenging problem in itself. Inspired by contemporary methods(https://hitz-zentroa.github.io/lm-contamination/blog/) we take the following heuristic approach: we prompt GPT-4 with a prefix of the question and instruct it to complete the remaining question providing context of the year (for eg, Complete this question from JEE Advanced 2017). In this, we check if the model is able to generate “precarious” data, that is, data which cannot be predicted from the context. For example, some numerical data provided, or the options of the questions. We use the same 50 questions sampled above and prompt GPT-4. Our evaluation suggested GPT-4 was unable to generate any such responses. \n\nThese facts suggest that the extent of contamination, if any, is very low and that is not detrimental to the paper. We will add these analyses in the final paper to clear any aspersions regarding possible contamination.\n\nWe additionally want to emphasize that our benchmark is dynamic in the sense that a new set of ~40-50 new uncontaminated problems can be added to it annually. This would allow a reliable test of the problem solving abilities of future LLMs as more and more data goes into training them. \n\n**Adding calculation tools**\n\nThe JEE exam only allows access to a basic calculator with 4 primitive arithmetic operation(+, -, /, *). Using plugins such as Wolfram or python would not make it a fair comparison(for example, directly asking Wolfram to integrate a function instead of doing it from first principles). Instead, to ensure a level-playing field, we use the newly introduced [function-calling API](https://openai.com/blog/function-calling-and-other-api-updates) by GPT-4 to implement standard arithmetic operators. \n\nNote that function-calling is only allowed with gpt-4-0613 whereas our initial results were with gpt-4-0314. We first evaluate gpt-4-0613 with zero-shot CoT. Surprisingly, results suggest that gpt-4-0613 is weaker than the previous model with a CoT performance of 0.303 as compared to 0.350 with gpt-4-0314.\n\nUsing a calculator API reduces performance even more to 0.274 from 0.303. We observe that tool usage is not very robust for GPT-4, where it sometimes hallucinates invalid function arguments and sometimes even invalid function names! Also, GPT-4 is quite accurate at arithmetic for small digit operations. Computation errors are mostly at a “symbolic” level than at an arithmetic level which is probably why a black-box calculator might not be very beneficial. We hypothesize that the way gpt-4 integrated function-calling might be degrading the prompt which leads to worse performance. \n\n\n**Blurred Examples**\n\nWe apologize to the reviewers for the blurred images. We would replace them with higher fidelity images in the final version of the paper. \n\n\n**Missing references**\n\nWe thank the reviewers for suggesting more references in the manuscript. We find C-Eval and SciBench to be very relevant concurrent works. C-Eval is a dataset built on problems in Chinese, derived from Chinese college and professional exams. SciBench is made along similar lines borrowing data from famous books on Physics, Chemistry and Mathematics. The paper on Exploring the MIT EECS curriculum attempts similar analysis on questions taken from MIT Exams. We will cite all these papers.\n\nGalactica is a language model which has been fine-tuned on a lot of technical data such as papers, knowledge bases and reference materials and shows significant improvements over GPT-3 and PaLM on relevant datasets such as MMLU and Math. However, the model is not publicly available, which is why we can't run inference on it. Even then, we find it is very relevant and will cite it.", "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": "In the era of LLM, a comprehensive benchmark is very necessary for NLP community. This paper focus on IIT JEE-Advanced exam, which is both hard and broad enough to test different capabilities of LLM.", "reasons_to_reject": "Overall I think it's a good paper, with nice data and thorough analysis.\n\nOnly concern is whether the dataset might be appeared in training corpus of LLM, which I suggest authors provide a few justification.", "questions_to_authors": "1. Could authors provide some justification that the exam & answer they used are not easily accessed in LLM pre-training? (e.g., answer is located in different places from question)\n2. The authors mention the calculation capability of LLM. Could the authors try adding some calculation tools (like python / wolfram alpha) to LLM and see its performance?\n3. The examples are blurred. It would be better to provide clear examples for such a dataset paper.", "paper_topic_and_main_contributions": "This paper proposes a new LLM benchmark called JEEBench, which comprises of pre-engineering mathematics, physics, and chemistry problems derived from the highly competitive IIT JEE-Advanced exam, testing LLM's long-horizon reasoning and in-depth domain knowledge. The paper also introduces an innovative method for effective response selection by applying confidence-thresholding over self-consistency. The authors have identified and detailed the common failure modes of GPT-4, such as errors in algebraic manipulation, difficulty in translating abstract concepts into mathematical equations, and failing to retrieve relevant domain-specific concepts.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "NA"}, "extra_text": {"Missing_References": "There are a few concurrent works when this paper is submitted, including:\n[1] C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models\n[2] SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models \n[3] Exploring the MIT Mathematics and EECS Curriculum using Large Language Models\n\nand one older work:\n[4] Galactica: A Large Language Model for Science\n\nThese works also provide some scientific questions. I think it's better to highlight some difference of this work compared with them."}}, {"review_id": "AoExOEczSA", "reviewer": "Reviewer_Kktx", "summary": "", "questions": "- E. Will the dataset *and* code for experiments be made available to the public? There is no link.\n- F. I'm concerned about the copyrights of the data, as it is not addressed. Do you own, or have obtained permission to publish, the data?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the generous and detailed review. We are thrilled by the fact that they found our work important for assessing LLMs, our discussions thorough and our evaluation extensive. We apologize for the low fidelity of the images. We will work on improving them in the final version of the paper.\n\nThe following points provide responses to the reviewer’s questions.  \n\n**Few shot prompting:**\n\nWe perform the following experiment for few shot prompting:\n\nFor each (subject, question_type) pair, we collect an example question from JEE Advanced 2014 and write their solutions. These are then used for 1-shot prompting. Overall, 1-Shot CoT achieves a score of 0.296 as opposed to zero-shot CoT(Just adding \"Let's think steo by step\") at 0.350 and vanilla GPT-4 at 0.308. Our hypothesis is that Few-shot prompting is not very helpful in these questions because conceptual errors are hard to be improved upon using few-shot examples. Additionally, many novel reasoning paths might not be covered in the few-shot examples. Thus, our dataset acts as an interesting testbed for advanced approaches in few-shot prompting. Similar results where results are better with zero-shot CoT as compared to few-shot CoT have been found in [SciBench](https://arxiv.org/abs/2307.10635)(See Table 3). We intend to add this discussion to our paper.\n\n**Difference between models**\n\nWe would like to draw the reviewer’s attention towards Table 2 where we provide numerical comparison of performance on JEEBench across various open-source and proprietary LLMs. We qualitatively compare these in Section 4.2 under the subsection “General Trends''.  Our observation is that open-source models perform as good as random and are, in general, lagging behind proprietary models. Performance on JEEBench increases consistently with newer versions of the GPT model. GPT-3 exhibits near random performance, but GPT-3.5 and GPT-4 perform significantly better, GPT-4 being far superior. It is evident that the performance boost is the highest for Chemistry, followed by Physics, and lastly Maths. This is probably because the complexity of reasoning is highest in Mathematics questions and least in Chemistry in JEEBench.\n\n**Effect of instruction tuning**\n\nInstruction-tuning can be done in many ways, one of them being PPO using a reward model as shown in the [InstructGPT](https://arxiv.org/abs/2203.02155) paper. For this aspect of LLMs, we perform inference using davinci-002, which is the base model for GPT-3. text-davinci-003 has been derived from davinci-002 using PPO on a reward model trained from human-feedback as documented by OpenAI [here](https://platform.openai.com/docs/model-index-for-researchers/instructgpt-models). The responses generated show that davinci-002 almost never reach the answer when prompted with the instruction (such as, \"In this problem, only one option will be correct. Give a detailed solution and end the solution with the final answer\"). Therefore, its performance is extremely poor (near zero) as compared to text-davinci-003. We will add this analysis to our paper.\n\n**Random performance**\n\nWe would like to point out that we have already added the random performance of the models in Table 2. We would include the normalized metric: (accuracy-p)/(1-p) in the final version of the paper.\n\n**Will code and dataset, both be released?**\n\nYes. We will add the link to the code, dataset and evaluation scripts in the final version of the paper.\n\n**Permissions to use this data**\n\nThe JEE Examination is a national exam which is conducted every year, and the exam papers are distributed for the general public (https://www.jeeadv.ac.in/archive.html) and has been used by various publishers and institutes for commercial purposes already. We also have clarifications from official sources that we are allowed to use this data since it is publicly available.\n\n**Missing references for calibration studies**\n \nWe apologize for not providing adequate references for our calibration study. We would add these in the final version of the paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The benchmark evaluates many high-level problem-solving abilities (domain-specific concepts, mathematical grounding and computing) and is sufficiently challenging to effectively differentiate current language models and leaves much room for improvement by future LLMs. This is very important for assessing and guiding the development of LLMs.\n- The authors adequately discuss contamination through the inclusion of recent data and separate model evaluation.\n- Extensive evaluation of current models from various aspects (performance, calibration, assessment of self-knowledge), covering a wide range of popular techniques (chain-of-thought, self-consistency, self-critiquing).\n- Extensive qualitative evaluation of error cases regarding not only the initial solutions but also the self-critiques. The taxonomy of errors provided by the authors is notably clear and insightful, which makes the qualitative analysis informative and easy to read.\n- The paper is very well-written.", "reasons_to_reject": "- A. Few-shot prompted evaluations are not provided. This is important to validate the utility of this benchmark, i.e., whether it is challenging enough. Though the authors cite cost and time as a reason for this omission in Line 270, however, the authors include experiments on self-consistency with 8 repetitions, which I expect would cost more than 8-shot prompting. Also, I do not believe that inference on 500 test samples would take too long, even with the longer length. Results on few-shot prompting (even with very few e.g. 1, 2) would improve the validity of the challenge and provide insights on the effect of few-shot prompting. Note that current results do already *suggest* sufficient difficulty.\n- B. There is limited analysis of the difference in performance *between* different models. Some analysis, or at least a discussion on the reasons behind the performance trends may be insightful.\n\n### Minor\n\n- C. It would be interesting to see the effect of instruction tuning on model performance. Despite mentioning that the results \"demonstrate the \"efficacy of ... instruction fine-tuning and RLHF\" (Line 511), there is no comparison of vanilla and instruction-tuned models. Note, \"GPT-3\" used in the paper (text-davinci-003) is another *instruction-tuned* variant of GPT-3.\n- D. Multi-choice questions leave a significant chance for the models to simply guess the right answer (I assume that is why 28% of correct answers come from incorrect reasoning, Line 343). It would help readers understand the true performance of models if you could include baseline random-chance performance, or normalize the scores based on random-chance probability p via (accuracy - p) / (1 - p), following other papers such as BIG-Bench.", "questions_to_authors": "- E. Will the dataset *and* code for experiments be made available to the public? There is no link.\n- F. I'm concerned about the copyrights of the data, as it is not addressed. Do you own, or have obtained permission to publish, the data?", "paper_topic_and_main_contributions": "The authors introduce a benchmark based on the IIT JEE-Advanced exam, which provides a sufficient challenge for current LLMs, including GPT-4 and PaLM-2. The authors provide a comprehensive evaluation of the latest open- and closed-source LLMs, using greedy decoding, as well as popular state-of-the-art techniques such as self-consistency and self-critiquing. Here, the authors provide a well-organized and extensive qualitative study on errors in initial solutions *and* critiques, using 100 random samples. In addition to accuracy and calibration performance, the authors probe the LLMs' ability to assess their own abilities and risk, through evaluation under a negative marking scheme (i.e., negative scores for wrong answers, with option to abstain). The authors consider a prompt-based solution and confidence thresholding (based on the frequency of answers from repeated inferences) and find that the latter is effective, and the former is not.\n\nIn summary, the paper introduces a challenging benchmark that looks to be useful for evaluating the problem solving and self-knowledge-assessing abilities of current and future language models. The paper provides an extensive analysis of current models based on the proposed benchmark and various popular inference methods.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "- Discussion and references of previous works on calibration and risk assessment are warranted. For example, [1] thoroughly addresses calibration and self-knowledge assessment and confirms the high calibration ability of LLMs. Discussion in Line 539 could also be expanded to address findings from recent work.\n\n[1] https://arxiv.org/pdf/2207.05221.pdf", "Typos_Grammar_Style_and_Presentation_Improvements": "- Figures have low image resolution. They are slightly blurry on print and high-res displays. Authors could consider non-raster graphics.\n\n### Complements\n\n- I like that you put GPT-4 responses in the text of the Appendix rather than a figure, as many papers do. I think it's more readable!"}}, {"review_id": "mLgGZUyaVc", "reviewer": "Reviewer_8fFX", "summary": "", "questions": "See reasons to reject", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their positive comments. We are glad that they found our methodology robust, our analysis interesting. \n\nThe following points provide responses to the reviewer's questions: \n- **Removal of questions with images**: We focused on text-only questions in this paper with the motivation that the reasoning ability of text-only models has improved very significantly compared to multimodal vision-language models. That said, we emphasize that collecting image-based questions is indeed feasible and we keep this for future work.\n\n- **Public release of the dataset**: Yes, the dataset will be completely publicly released. The data is purely meant as a test-set. There is no train/val split for the data. What we meant by the planned release mechanism was that the dataset could be updated every year with new questions pouring in from the examination.", "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": "We're running into the limits of MMLU and other broad-coverage datasets evaluating model reasoning. We desperately need more good, uncontaminated datasets to evaluate LLM reasoning. The methodology appears solid; the analysis is very interesting (e.g., the discussion around self-critique and contamination); the writing is good.", "reasons_to_reject": "I am a bit disappointed that problems with diagrams were eliminated. It would have been nice to have these separately included, especially with the emergence of more and more multimodal models. It's also a bit unclear to me what the planned release mechanism is for this dataset. Is it going to be completely publicly released? Is the test answer set going to be held out like in SuperGLUE?", "questions_to_authors": "See reasons to reject", "paper_topic_and_main_contributions": "The paper's primary contribution is a dataset of IIT JEE-Advanced questions, many of which appear to require sophisticated step-by-step reasoning and a fairly broad and thorough analysis of SoTA models on this dataset.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes a new LLM benchmark called JEEBench, which comprises of pre-engineering mathematics, physics, and chemistry problems derived from the highly competitive IIT JEE-Advanced exam, testing LLM's reasoning and in-depth domain knowledge. The paper also introduces an innovative method for effective response selection by applying confidence-thresholding over self-consistency. The authors have identified and detailed the common failure modes of GPT-4, such as errors in algebraic manipulation, difficulty in translating abstract concepts into mathematical equations, and failure to retrieve relevant domain-specific concepts.

In the era of LLM, a comprehensive benchmark is very necessary for NLP community. This paper focuses on IIT JEE-Advanced exam, which is both hard and broad enough to test different capabilities of LLM.

All reviewers found the paper strong and exciting.
Decision: Accept-Main