Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers

## Abstract

Abstract Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas.
Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire research process.
We address this by establishing an experimental design that evaluates research idea generation while controlling for confounders and performs the first head-to-head comparison between expert NLP researchers and an LLM ideation agent.
By recruiting over 100 NLP researchers to write novel ideas and blind reviews of both LLM and human ideas, we obtain the first statistically significant conclusion on current LLM capabilities for research ideation:
we find LLM-generated ideas are judged as more novel ( p < 0.05 𝑝 0.05 p<0.05 italic_p < 0.05 ) than human expert ideas while being judged slightly weaker on feasibility.
Studying our agent baselines closely, we identify open problems in building and evaluating research agents, including failures
of LLM self-evaluation and their lack of diversity in generation.
Finally, we acknowledge that human judgements of novelty can be difficult, even by experts, and propose an end-to-end study design which recruits researchers to execute these ideas into full projects, enabling us to study whether these novelty and feasibility judgements result in meaningful differences in research outcome. 1 1 1 Interested researchers can sign up for this end-to-end study at: https://tinyurl.com/execution-study . We release our agent implementation and all human review scores at: https://github.com/NoviScl/AI-Researcher . ∗ The last two authors advised this project equally.

## 1 Introduction

The rapid improvement of LLMs, especially in capabilities like knowledge and reasoning, has enabled many new applications in scientific tasks, such as solving challenging mathematical problems Trinh et al. (), assisting scientists in writing proofs Collins et al. (), retrieving related works Ajith et al. (), Press et al. (), generating code to solve analytical or computational tasks Huang et al. (), Tian et al. (), and discovering patterns in large text corpora Zhong et al. (), Lam et al. ().
While these are useful applications that can potentially increase the productivity of researchers, it remains an open question whether LLMs can take on the more creative and challenging parts of the research process.

Figure: Figure 1: Overview of our study: we recruit 79 expert researchers to perform blind review of 49 ideas from each of the three conditions: expert-written ideas, AI-generated ideas, and AI-generated ideas reranked by a human expert. We standardize the format and style of ideas from all conditions before the blind review. We find AI ideas are judged as significantly more novel than human ideas ($p<0.05$).
Refer to caption: x1.png

Figure: Figure 2: Comparison of the three experiment conditions across all review metrics. Red asterisks indicate that the condition is statistically better than the Human baseline with two-tailed Welch’s t-tests and Bonferroni correction. All scores are on a 1 to 10 scale. More detailed results are in Section 5.
Refer to caption: x2.png

We focus on this problem of measuring the *research ideation* capabilities of LLMs and ask: are current LLMs capable of generating novel ideas that are comparable to expert humans? Although ideation is only one part of the research process, this is a key question to answer, as it is the very first step to the scientific research process and serves as a litmus test for the possibility of autonomous research agents that create their own ideas.
Evaluating expert-level capabilities of LLM systems is challenging Bakhtin et al. (), Collins et al. (), and research ideation takes this to an extreme. Qualified expert researchers are difficult to recruit at scale, evaluation criteria can be highly subjective, and it is difficult for even the best experts to judge the quality of an idea Beygelzimer et al. (), Simsek et al. ().

We address these challenges directly, recognizing that for important, high-stakes tasks like research ideation, there is no substitute for a large-scale expert evaluation. We design a carefully controlled comparison of human and LLM ideas that overcomes sample size and baseline problems present in earlier small-scale evaluation studies. Our study recruited a large pool of over 100 highly qualified NLP researchers to produce human baseline ideas and perform blind reviews of human and LLM ideas. To reduce the possibility that confounding variables affect our outcome measures, we enforce strict controls that standardize the styles of human and LLM ideas and match their topic distribution.

We compare our human expert baseline with a simple and effective LLM agent that incorporates retrieval augmentation and adopts recent ideas in inference-time scaling, such as overgenerating and reranking LM outputs.
These measures allow us to make statistically rigorous comparisons between human experts and state-of-the-art LLMs (Figure 1).

Our evaluation-centric approach complements many recent methods-centric works that attempt to instantiate research agents Wang et al. (), Baek et al. (), Yang et al. (), Lu et al. (), Li et al. ().
The majority of these works rely on fast and lower-cost evaluation surrogates – either by decreasing the number of expert reviewers Baek et al. (), Li et al. (), Wang et al. (), Yang et al. (), constraining the length and detailedness of the ideas Wang et al. (), Yang et al. (), or relying on LLM-as-a-judge Lu et al. ().
They do not perform the large-scale human comparison studies that are needed to answer the motivating question of our work. Our work takes the opposite approach, performing a year-long and high-cost evaluation that provides human expert baselines and a standardized evaluation protocol to serve as a foundation for future follow-up studies and methods work.

Through nearly 300 reviews across all our conditions, we find that AI-generated ideas
are judged as more novel than human expert ideas ($p<0.05$), which holds robustly under multiple hypothesis correction and across different statistical tests.
We find some signs that these gains are correlated with excitement and overall score, and may come at the slight expense of feasibility, but our study size did not have sufficient power to conclusively identify these effects (Figure 2).

Qualitative analysis of free-text responses in our review corroborates these findings on novelty and feasibility.
Apart from evaluating the ideas, we also analyze the LLM agent, showing limitations and open problems – despite excitement about inference-time scaling of LLMs, we find that they lack idea diversity when we scale up idea generation, and they cannot currently serve as reliable evaluators.

## 2 Problem Setup

The central experiment of our work is a comparison of human- and LLM-generated ideas. While this goal is simple, there is no existing consensus on how to formulate the task of research ideation and evaluation, and we begin by defining the key aspects of our experiment design.

We think of research idea evaluation as consisting of three separate components: 1). the idea itself, generated in response to our instructions, 2). the writeup which communicates the idea, and 3). the evaluation of the writeup by experts. We outline our experiment design in each of these three parts with particular focus on potential confounders, such as the area of research, the format of a research idea, and the evaluation process.

#### Ideation Scope and Instructions

Research ideas can take many different forms. They can be simple tricks to improve model performance, or they may be large-scale research programs that form the basis of a Ph.D. thesis. Any experiment on ideation must carefully balance the realisticness and interestingness of a research idea with the practical realities of eliciting ideas from a large population. In our case, these tradeoffs are even more pronounced, as we have designed our ideation experiments so that the resulting ideas can be executed by experts in a follow-up set of experiments.

These constraints have led us to study prompting-based NLP research as a testbed for our study. Prompting research has been popular in recent years of NLP and AI research (e.g., Wei et al., , Si et al., , Wang et al., , Zhou et al., , Chen et al., , Madaan et al., , Yasunaga et al., , Yao et al., , Diao et al., , Qin et al., , Schulhoff et al., , inter alia). This class of projects strikes a reasonable trade-off among our constraints. The most impactful prompting projects like chain-of-thought have had a major influence on LLM performance (Wei et al., ), and prompting projects are executable with minimal computing hardware.

We further structure our ideation process to avoid selection-bias-based confounders in ideation. If we simply ask LLMs and humans to produce ideas on ‘prompting topics’, we may find that LLMs and humans differ in the types of research ideas they produce (for example, LLMs may naturally suggest more projects on safer topics, which might be judged as less exciting by humans). This would lead us to simply measure misalignment in research topic preference between LLMs and humans, which is not the goal of our study. To address this possibility, we define a set of seven specific research topics extracted from the Call For Papers page of recent NLP conferences such as COLM. (^2^22[https://colmweb.org/cfp.html](https://colmweb.org/cfp.html)) Specifically, our topics include: Bias, Coding, Safety, Multilinguality, Factuality, Math, and Uncertainty (see Appendix A for a complete description of these topics).

Each human and LLM participant of the ideation experiment receives the same set of natural language instructions including the same topic description, idea template, and demonstration example to ensure a fair comparison. For human participants, we additionally allow them to select a preferred topic from the list, and for each selected topic, we generate a corresponding LLM idea. This exactly matches the idea topic distribution between the LLM and human participants, while ensuring that human experts are able to select topics according to their expertise.

#### Idea Writeup

An idea can only be evaluated if it is written up to be communicated, but this writing process introduces many additional potential confounders. Human researchers may write in ways that subtly signal quality research, such as including more examples and implementation details.
The format of the writeup functions as a way to scaffold what contents should be included and the level of detailedness.
Ideally, we want both human and LLM participants to provide all the necessary implementation details for their generated ideas.

We take inspiration from guidelines used in grant submissions and introduce a template to specify the structure and detailedness of idea proposals. Specifically, we construct a template that includes fields for the title, problem statement, motivation, proposed method, step-by-step experiment plan, test case examples, and the fallback plan.
Both the LLM agent and the human idea writers are instructed to follow this template and our provided demonstration examples to produce a project proposal as the output (see Appendix B for the full template and Appendix C for the demo example).

Even with these templates, there may be subtle writing style cues that affect the outcome measure. For example, humans may tend to write in a more engaging and informal tone. To reduce this possibility further, we developed a style normalization module that uses an LLM to convert all ideas into the same writing and formatting style without changing the original content.
Our small-scale human study shows that such a normalization approach leads to a 50% accuracy for expert human judges who are asked to distinguish AI ideas from human ideas.
Finally, the use of an LLM style anonymizer has the possibility of substantively changing the content of the ideas. To rule this out, the first author of this paper manually verified each human idea proposal to ensure all contents of the original ideas were preserved.
We present the full prompt used in Appendix D.

#### Review and Evaluation

Reviewing research ideas is notoriously subjective, so we want to design a review form that defines all review criteria clearly to standardize and anchor the evaluations as much as possible. At the same time, we want our review criteria and measured variables to capture all the desiderata of high-quality research ideas.

We follow best practices from AI conference reviewing (e.g., ICLR and ACL) when designing the review form, where we define four breakdown metrics including novelty, excitement, feasibility, and expected effectiveness, apart from the overall score. For each metric, we ask for a numerical score on a 1-10 scale along with a free-text rationale. We provide clear definitions and grounding for each numerical scale to calibrate all reviewers’ standards (see Appendix E for the full review form).

Our blind review evaluation will compare ideas from three different conditions:

- 1.
Human Ideas: Idea proposals written by our recruited expert researchers.
- 2.
AI Ideas: Idea proposals generated by our LLM agent. We directly take the top-ranked ideas from the agent’s output.
- 3.
AI Ideas + Human Rerank: Idea proposals generated by our LLM agent. The first author of this paper manually selected the top-ranked ideas out of all the LLM agent’s generations rather than relying on the LLM ranker in order to better estimate the upper-bound quality of AI ideas.

In the next two sections, we instantiate how our LLM agent generates ideas and how our expert participants generate and review the ideas.

## 3 Idea Generation Agent

We build a simple but effective LLM ideation agent to compare with the human expert baseline.
Rather than focusing on innovating the agent itself, we adhere to a minimalist design principle, aiming to understand the current capabilities of LLMs in idea generation.
Our research ideation agent has three essential components:
paper retrieval, idea generation, and idea ranking, which we will describe in detail below.

### 3.1 Paper Retrieval for RAG

To ground idea generation, the agent needs to retrieve papers related to the given research topic, so that it will be aware of related works when generating new ideas.
To do so, we leverage retrieval-augmented generation (RAG), which has demonstrated effectiveness on many knowledge-intensive tasks  Lewis et al. (), Shi et al. ().
Concretely, given a research topic (e.g., “novel prompting methods that can improve factuality and reduce hallucination of
large language models"), we prompt an LLM to generate a sequence of function calls to the Semantic Scholar API. We use claude-3-5-sonnet-20240620 as the backbone model for our agent but the pipeline should generalize to other LLMs as well. The paper retrieval action space includes: {KeywordQuery(keywords), PaperQuery(paperId), GetReferences(paperId)}. Each action generation is grounded on the previous actions and executed results. We keep the top $k=20$ papers from each executed function call and stop the action generation when a max of $N=120$ papers have been retrieved. We then use the LLM to score and rerank all retrieved papers based on three criteria: 1) the paper should be directly relevant to the specified topic; 2) the paper should be an empirical paper involving computational experiments;(^3^33Note that we exclude position papers, survey papers, and analysis papers throughout this study since their evaluation tends to be very subjective.) 3) the paper is interesting and can inspire new projects. The LLM is prompted to score each retrieved paper on a scale of 1 to 10 based on these criteria and we use the top-ranked papers for the next step of idea generation.

### 3.2 Idea Generation

Our key insight for idea generation is to generate as many candidate ideas as possible. Our intuition is that only a small fraction of all generated ideas might be high-quality, and we should be willing to expend inference-time compute to generate more candidates so that we can later use a reranker to discover the "diamond in the rough". This aligns with existing results showing that scaling inference compute with repeated sampling can boost LLM performance on various coding and reasoning tasks Li et al. (), Brown et al. ().
Specifically, we prompt the LLM to generate 4000 seed ideas on each research topic. The idea generation prompt includes the demonstration examples and the retrieved papers.
We craft $k=6$ demonstration examples by manually summarizing exemplar papers Yasunaga et al. (), Madaan et al. (), Weller et al. (), Weston and Sukhbaatar (), Zheng et al. (), Dhuliawala et al. () into our desired idea format.
For retrieval augmentation, we randomly select $k=10$ papers from the top-ranked retrieved papers and concatenate their titles and abstracts to prepend to the idea generation prompt.
We also append the titles of all previously generated ideas to the prompt to explicitly ask the LLM to avoid repetitions.

To remove duplicated ideas from this large pool of candidate ideas, we first perform a round of deduplication by encoding all seed ideas with all-MiniLM-L6-v2 from Sentence-Transformers Reimers and Gurevych () and then computing pairwise cosine similarities. We set a similarity threshold of 0.8 for the idea deduplication based on manual inspection. (^4^44We provide randomly sampled idea pairs and their similarities in Appendix H. We also provide additional implementation details about the ideation agent in Appendix F.)
This leaves about 5% non-duplicated ideas out of all the generated seed ideas. We expand more on this duplication issue later in Section 7.1.

### 3.3 Idea Ranking

The next step is for our ideation agent to rank all the remaining ideas so that we can find the best ones among them.
To build such an automatic idea ranker, we use public review data as a proxy. Specifically,
we scraped 1200 ICLR 2024 submissions related to LLMs (with keyword filtering) along with their review scores and acceptance decisions. We explored multiple ways of predicting the scores and decisions of these submissions and found that LLMs are poorly calibrated when asked directly to predict the final scores or decisions, but can achieve non-trivial accuracy when asked to judge which paper is better in pairwise comparisons.

**Table 1: Average ICLR review scores of top- and bottom-10 papers ranked by our LLM ranker, with different rounds ($N$) of pairwise comparisons.**
| $N$ | Top-10 | Bottom-10 | Gap |
| --- | --- | --- | --- |
| 1 | 6.28 | 5.72 | 0.56 |
| 2 | 6.14 | 5.24 | 0.90 |
| 3 | 5.83 | 4.86 | 0.97 |
| 4 | 5.94 | 4.99 | 0.95 |
| 5 | 6.42 | 4.69 | 1.73 |
| 6 | 6.11 | 4.81 | 1.30 |

We converted the ICLR submissions into our standard project proposal format and randomly paired up accepted and rejected papers and asked LLMs to predict which one is accepted. On this task, Claude-3.5-Sonnet achieves an accuracy of 71.4% with zero-shot prompting. For comparison, GPT-4o achieves 61.1% and Claude-3-Opus achieves 63.5%, and we do not observe significant gains from additional prompting techniques like few-shot or chain-of-thought prompting.
We therefore choose the Claude-3.5-Sonnet zero-shot ranker.

In order to obtain reliable scores for all project proposals based on pairwise comparisons, we adopt a Swiss system tournament where all project proposals are paired with those whose accumulated scores are similar, and if the proposals are judged to be better, they gain an additional point. We repeat this for $N$ rounds so the total score of each project proposal will be within the [0, $N$] range. As a sanity check, we use the Claude-3.5-Sonnet ranker to rank the 1.2K ICLR LLM-related submissions and compare the average review scores of the top 10 ranked papers and the bottom 10 ranked papers in Table 1. We see a clear separation between the top and bottom ranked papers, indicating the effectiveness of the LLM ranker. We choose $N=5$ for all our experiments since it gives the best ranking result on this validation set.
The top-ranked project proposals from the agent will be directly used for the AI Ideas condition of the human study.

Since our AI ranker is still far from perfect, we also introduce another experiment condition where the first author of this paper manually reranked the generated project proposals instead of relying on the LLM ranker, and we call this the AI Ideas + Human Rerank condition.
As we show in Table 12, 17 out of the 49 ideas in the AI Ideas + Human Rerank condition overlap with the AI Ideas condition, while the other 32 are different, indicating the discrepancy between the LLM ranker and the human expert reranking.

## 4 Expert Idea Writing and Reviewing

In this section, we shift focus to the human branch of idea generation comparison. We present the details of our human study, including information about the recruited experts, the human idea generation task, and the subsequent review process.

### 4.1 Expert Recruitment

We recruit our expert participants (including for idea writing and reviewing) by sending sign-up forms to several channels, including: 1) the OpenNLP Slack channel with 1426 NLP researchers from 71 institutions (with consent from the channel manager); 2) Twitter (X); 3) Slack channels of various NLP groups by direct communication with the group members; and 4) official chat app of the NAACL 2024 conference. We also conducted in-person recruitment by giving out name cards and wearing T-shirts (^5^55[https://x.com/ChengleiSi/status/1804273510656749649](https://x.com/ChengleiSi/status/1804273510656749649)) with sign-up links at the NAACL 2024 conference as well as various other local NLP social events. Our study has been approved by the Stanford IRB (ID 74246).

We performed screening on all the US participants (^6^66We have to recruit participants located in the US due to logistical reasons.) based on their provided Google Scholar profiles. We set a minimum requirement of having published at least one paper at a major AI venue. (^7^77E.g., *ACL, NeurIPS, ICLR, ICML, AAAI.) We reached out to all participants who satisfied this requirement with the consent form and followed up with the annotation documents for those who consented to participate.

In the end, we recruited $N=49$ experts for writing ideas, and $N=79$ experts for reviewing ideas.
Note that 24 out of the 79 reviewers also participated in the idea writing, and we made sure no reviewer would review their own idea. This results in $N=104$ total participants across the two tasks.
Each idea writer is asked to write one idea within 10 days and we compensate $300 for each, with a $1000 bonus for the top 5 ideas as scored by the expert reviewers.
Each idea reviewer is assigned 2 to 7 ideas to review and we collected $N=298$ unique reviews in total.
They are given one week to finish the reviews and we compensated $25 for each review written by the idea reviewers.

### 4.2 Expert Qualifications

Figure: Figure 3: Positions of our idea writer (left) and reviewer (right) participants.
Refer to caption: x3.png

Our pool of participants is highly qualified and diverse. The 49 idea writers come from 26 different institutions (Table 15) and the majority of them are current PhD students (Figure 3 left).
The 79 reviewers come from 32 institutions (Table 16) and are mostly PhD students and Postdocs (Figure 3 right).
We use their Google Scholar profiles to extract several proxy metrics, including the number of papers, citations, h-index, and i10-index at the time of their submission. Table 2 shows that our idea writers have an average of 12 papers and 477 citations, while every reviewer has published at least two papers and has an average citation of 635 and h-index of 7.
Moreover, based on their survey responses, 72 out of the 79 reviewers have previously reviewed for major AI conferences or journals.
These statistics indicate that our participants are highly qualified and have substantial research experience. (^8^88Detailed breakdown of participant positions is in Appendix K.)

**Table 2: Research profile metrics of the idea writing and reviewing participants. Data are extracted from Google Scholar at the time of idea or review submission.**
|  | Idea Writing Participants (N=49) | Idea Reviewing Participants (N=79) |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Metric | Mean | Median | Min | Max | SD | Mean | Median | Min | Max | SD |
| papers | 12 | 10 | 2 | 52 | 9 | 15 | 13 | 2 | 52 | 10 |
| citations | 477 | 125 | 2 | 4553 | 861 | 635 | 327 | 0 | 7276 | 989 |
| h-index | 5 | 4 | 1 | 21 | 4 | 7 | 7 | 0 | 21 | 4 |
| i10-index | 5 | 4 | 0 | 32 | 6 | 7 | 5 | 0 | 32 | 6 |

**Table 3: Statistics of the 49 ideas from each condition.**
| Metric | Mean | Median | Min | Max | SD |
| --- | --- | --- | --- | --- | --- |
| Human Ideas |  |  |  |  |  |
| Familiarity (1-5) | 3.7 | 4.0 | 1.0 | 5.0 | 1.0 |
| Difficulty (1-5) | 3.0 | 3.0 | 1.0 | 5.0 | 0.7 |
| Time (Hours) | 5.5 | 5.0 | 2.0 | 15.0 | 2.7 |
| Length (Words) | 901.7 | 876.0 | 444.0 | 1704.0 | 253.5 |
| AI Ideas |  |  |  |  |  |
| Length (Words) | 1186.3 | 1158.0 | 706.0 | 1745.0 | 233.7 |
| AI + Human Rerank Ideas |  |  |  |  |  |
| Length (Words) | 1174.0 | 1166.0 | 706.0 | 1708.0 | 211.0 |

**Table 4: Idea topic distribution.**
| Topic | Count |
| --- | --- |
| Bias | 4 |
| Coding | 9 |
| Safety | 5 |
| Multilingual | 10 |
| Factuality | 11 |
| Math | 4 |
| Uncertainty | 6 |
| Total | 49 |

### 4.3 Idea Writing

We report statistics of our idea writers’ ideas to measure their quality.
As shown in Table 3, idea writers indicate a moderately high familiarity with their selected topic (3.7 on a 1 to 5 scale), and indicate the task as moderately difficult (3 on a 1 to 5 scale). They spent an average of 5.5 hours on the task and their ideas are 902 words long on average. These indicate that participants are putting substantial effort into this task. (^9^99See Appendix J for more details on the quality control of human ideas.)
We also show the distribution of their selected topics in Table 4.

### 4.4 Idea Reviewing

**Table 5: Statistics of the review assignment.**
| Metric | Mean | Min | Max | SD |
| --- | --- | --- | --- | --- |
| # Reviews | 3.8 | 2.0 | 7.0 | 1.3 |
| # Conditions | 2.5 | 2.0 | 3.0 | 0.5 |
| # Topics | 1.5 | 1.0 | 3.0 | 0.6 |

Review Assignment
We let all reviewer participants select their top two preferred topics as well as their preferred reviewing load (from 2 to 7).
We then randomly assign them to ideas within their selected topics and all ideas are anonymized.
In the assignment, we balance the number of ideas from each condition for each reviewer and ensure that each reviewer gets at least one human idea and one AI idea.
Every idea is reviewed by 2 to 4 different reviewers.
We also avoid assigning ideas written by authors from the same institution to avoid any potential contamination.
Table 5 shows that each reviewer wrote an average of 3.8 reviews from 2 or 3 conditions, across 1 to 3 topics.

Review Quality Check
Apart from ensuring reviewer qualifications, we also compute statistics to measure the quality of the reviews in Table 6.
On average, the reviewers indicated a familiarity of 3.7 (out of 5) in their selected topic and a confidence of 3.7 (out of 5) in their reviews. This is comparable with the 1.2K ICLR 2024 submissions related to language models, where the reviewers also have an average confidence of 3.7 out of 5. Moreover, reviewers spent an average of 32 minutes on each review, with each review being about 232 words long.

Since our review forms are different from the ICLR review forms, we compare them with the ICLR reviews where we remove the summary and question sections and only count the lengths of the strengths and weaknesses sections. This way, the ICLR reviews have an average length of 247, similar to our collected reviews.
As an additional measure of review quality, out of the 298 unique reviews that we have collected, 80 of them provided links to existing papers in their rationales to justify why the proposed method is not novel. These results further validate the high quality of our review data.

**Table 6: Statistics of our collected reviews, with ICLR 2024 reviews as a baseline (for the 1.2K submissions that mentioned the keyword “language models").**
| Metric | Mean | Median | Min | Max | SD |
| --- | --- | --- | --- | --- | --- |
| Ours |  |  |  |  |  |
| Familiarity (1-5) | 3.7 | 3.0 | 1.0 | 5.0 | 0.9 |
| Confidence (1-5) | 3.7 | 4.0 | 1.0 | 5.0 | 0.7 |
| Time (Minutes) | 31.7 | 30.0 | 5.0 | 120.0 | 16.8 |
| Length (Word) | 231.9 | 208.0 | 41.0 | 771.0 | 112.1 |
| ICLR 2024 |  |  |  |  |  |
| Confidence (1-5) | 3.7 | 4.0 | 1.0 | 5.0 | 0.8 |
| Length (Word) | 421.5 | 360.0 | 14.0 | 2426.0 | 236.4 |
| Length (Word; Strengths & Weaknesses) | 247.4 | 207.0 | 2.0 | 2010.0 | 176.4 |

## 5 Main Result: AI Ideas Are Rated More Novel Than Expert Ideas

In this section, we present our main finding on whether LLMs can generate better research ideas than experts. Consistently across three different statistical tests accounting for the possible confounders, we find that AI ideas have higher novelty scores than human ideas while being comparable on all other metrics.

**Table 7: Scores across all conditions by treating each review as an independent datapoint (Test 1). Size is the number of reviews for each condition and the p-values are computed with two-tailed Welch’s t-tests with Bonferroni correction. We bold results that are statistically significant (${}^{*}p<0.05;^{**}p<0.01;^{***}p<0.001$). AI ideas are judged as significantly better than human ideas in terms of novelty and excitement while being comparable on all other metrics.**
| Condition | Size | Mean | Median | SD | SE | Min | Max | p-value |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Novelty Score |  |  |  |  |  |  |  |  |
| Human Ideas | 119 | 4.84 | 5 | 1.79 | 0.16 | 1 | 8 | – |
| AI Ideas | 109 | 5.64 | 6 | 1.76 | 0.17 | 1 | 10 | 0.00** |
| AI Ideas + Human Rerank | 109 | 5.81 | 6 | 1.66 | 0.16 | 2 | 10 | 0.00*** |
| Excitement Score |  |  |  |  |  |  |  |  |
| Human Ideas | 119 | 4.55 | 5 | 1.89 | 0.17 | 1 | 8 | – |
| AI Ideas | 109 | 5.19 | 6 | 1.73 | 0.17 | 1 | 9 | 0.04* |
| AI Ideas + Human Rerank | 109 | 5.46 | 6 | 1.82 | 0.17 | 1 | 9 | 0.00** |
| Feasibility Score |  |  |  |  |  |  |  |  |
| Human Ideas | 119 | 6.61 | 7 | 1.99 | 0.18 | 1 | 10 | – |
| AI Ideas | 109 | 6.34 | 6 | 1.88 | 0.18 | 2 | 10 | 1.00 |
| AI Ideas + Human Rerank | 109 | 6.44 | 6 | 1.63 | 0.16 | 1 | 10 | 1.00 |
| Expected Effectiveness Score |  |  |  |  |  |  |  |  |
| Human Ideas | 119 | 5.13 | 5 | 1.76 | 0.16 | 1 | 8 | – |
| AI Ideas | 109 | 5.47 | 6 | 1.58 | 0.15 | 1 | 10 | 0.67 |
| AI Ideas + Human Rerank | 109 | 5.55 | 6 | 1.52 | 0.15 | 1 | 9 | 0.29 |
| Overall Score |  |  |  |  |  |  |  |  |
| Human Ideas | 119 | 4.68 | 5 | 1.90 | 0.17 | 1 | 9 | – |
| AI Ideas | 109 | 4.85 | 5 | 1.70 | 0.16 | 1 | 9 | 1.00 |
| AI Ideas + Human Rerank | 109 | 5.34 | 6 | 1.79 | 0.17 | 1 | 9 | 0.04* |

**Table 8: Scores across all conditions by averaging the scores for each idea and treating each idea as one data point (Test 2). Size is the number of ideas for each condition, and the p-values are computed with two-tailed Welch’s t-tests with Bonferroni correction. We bold results that are statistically significant (${}^{*}p<0.05;^{**}p<0.01$). AI ideas are judged as significantly better than human ideas in terms of novelty while being comparable on all other metrics.**
| Condition | Size | Mean | Median | SD | SE | Min | Max | p-value |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Novelty Score |  |  |  |  |  |  |  |  |
| Human Ideas | 49 | 4.86 | 5.00 | 1.26 | 0.18 | 1.50 | 7.00 | – |
| AI Ideas | 49 | 5.62 | 5.50 | 1.39 | 0.20 | 1.50 | 8.33 | 0.03* |
| AI Ideas + Human Rerank | 49 | 5.78 | 6.00 | 1.07 | 0.15 | 3.00 | 8.33 | 0.00** |
| Excitement Score |  |  |  |  |  |  |  |  |
| Human Ideas | 49 | 4.56 | 4.33 | 1.16 | 0.17 | 2.00 | 7.00 | – |
| AI Ideas | 49 | 5.18 | 5.50 | 1.33 | 0.19 | 2.50 | 7.33 | 0.08 |
| AI Ideas + Human Rerank | 49 | 5.45 | 5.50 | 1.36 | 0.19 | 1.00 | 7.33 | 0.00** |
| Feasibility Score |  |  |  |  |  |  |  |  |
| Human Ideas | 49 | 6.53 | 7.00 | 1.50 | 0.21 | 3.00 | 9.00 | – |
| AI Ideas | 49 | 6.30 | 6.00 | 1.27 | 0.18 | 2.50 | 8.50 | 1.00 |
| AI Ideas + Human Rerank | 49 | 6.41 | 6.50 | 1.06 | 0.15 | 4.00 | 9.00 | 1.00 |
| Expected Effectiveness Score |  |  |  |  |  |  |  |  |
| Human Ideas | 49 | 5.10 | 5.33 | 1.14 | 0.16 | 3.00 | 7.00 | – |
| AI Ideas | 49 | 5.48 | 5.50 | 1.23 | 0.18 | 2.00 | 7.50 | 0.58 |
| AI Ideas + Human Rerank | 49 | 5.57 | 5.50 | 0.99 | 0.14 | 3.00 | 7.50 | 0.17 |
| Overall Score |  |  |  |  |  |  |  |  |
| Human Ideas | 49 | 4.69 | 4.67 | 1.16 | 0.17 | 2.00 | 6.67 | – |
| AI Ideas | 49 | 4.83 | 5.00 | 1.34 | 0.19 | 1.50 | 7.50 | 1.00 |
| AI Ideas + Human Rerank | 49 | 5.32 | 5.50 | 1.24 | 0.18 | 2.00 | 7.50 | 0.06 |

**Table 9: Mean score differences between AI ideas and human ideas by treating each reviewer as a data point (Test 3). All p-values are computed with one-sample t-tests with Bonferroni correction. We bold results that are statistically significant (${}^{*}p<0.05;^{**}p<0.01$).**
|  | N | Mean Diff | p-value |
| --- | --- | --- | --- |
| Novelty Score |  |  |  |
| AI Ideas vs Human Ideas | 70 | 0.94 | 0.00** |
| AI Ideas + Human Rerank vs Human Ideas | 65 | 0.86 | 0.00** |
| Excitement Score |  |  |  |
| AI Ideas vs Human Ideas | 70 | 0.73 | 0.01* |
| AI Ideas + Human Rerank vs Human Ideas | 65 | 0.87 | 0.00** |
| Feasibility Score |  |  |  |
| AI Ideas vs Human Ideas | 70 | -0.29 | 0.36 |
| AI Ideas + Human Rerank vs Human Ideas | 65 | -0.08 | 0.74 |
| Effectiveness Score |  |  |  |
| AI Ideas vs Human Ideas | 70 | 0.42 | 0.16 |
| AI Ideas + Human Rerank vs Human Ideas | 65 | 0.39 | 0.16 |
| Overall Score |  |  |  |
| AI Ideas vs Human Ideas | 70 | 0.24 | 0.36 |
| AI Ideas + Human Rerank vs Human Ideas | 65 | 0.66 | 0.01* |

### 5.1 Test 1: Treating Each Review as an Independent Datapoint

In Test 1, we treat each review as an independent datapoint and aggregate all reviews from the same condition.
We treat the Human Ideas as the baseline condition and compare it with AI Ideas and AI Ideas + Human Rerank using two-tailed Welch’s t-tests with Bonferroni correction.
We show the barplot in Figure 2 and the detailed numerical results in Table 7.
Both AI Ideas ($\mu=5.64\pm\sigma=1.76$) and AI Ideas + Human Rerank ($\mu=5.81\pm\sigma=1.66$) are significantly better than Human Ideas ($\mu=4.84\pm\sigma=1.79$) on the novelty score ($p<0.01$).
In this particular test, the AI ideas in both conditions are also significantly better than human ideas on the excitement score ($p<0.05$), and the AI Ideas + Human Rerank condition is also significantly better than Human Ideas in terms of the overall score ($p<0.05$).
We do not observe significant differences between AI-generated ideas and human-written ideas on the other metrics.

### 5.2 Test 2: Treating Each Idea as an Independent Datapoint

Since we collect multiple reviews for each idea, one could argue that we should not treat each review as an independent datapoint. To account for this potential confounder, we perform Test 2 where we average the scores of each idea and treat each idea as one datapoint. This way, the sample size for every condition will be $N=49$, namely the number of ideas.
We treat the Human Ideas as the baseline condition and compare it with AI Ideas and AI Ideas + Human Rerank using two-tailed
Welch’s t-tests with Bonferroni correction.
As shown in Table 8, we still see significant results ($p<0.05$) where both AI Ideas ($\mu=5.62\pm\sigma=1.39$) and AI Ideas + Human Rerank ($\mu=5.78\pm\sigma=1.07$) have higher novelty scores than Human Ideas ($\mu=4.86\pm\sigma=1.26$).

### 5.3 Test 3: Treating Each Reviewer as an Independent Datapoint

Another possible confounder is that different reviewers might have different biases, for example, some reviewers may be more lenient than others. To account for such reviewer biases, we perform Test 3 where we treat each reviewer as one datapoint and compute their average score on each condition. Then for each reviewer, we get their mean score difference between the AI Ideas condition and the Human Ideas condition, as well as the difference between the AI Ideas + Human Rerank condition and the Human Ideas condition. This way, we only analyze the differences among the different conditions. That is, if the differences are significantly higher than zero under the one-sample t-test, that indicates reviewers are giving higher scores to one condition compared to the other.
The results are shown in Table 9, and we see significant results ($p<0.05$) that AI ideas in both the AI Ideas and AI Ideas + Human Rerank conditions are rated more novel than Human Ideas. Therefore, we conclude that AI ideas generated by our ideation agent are judged as more novel than human expert generated ideas, consistently across all three different statistical tests. (^10^1010We also include results of fitting linear mixed-effects models in Appendix N, which reinforces our conclusions. Additionally, we plot the breakdown of all metrics by topic in Appendix O.)

## 6 In-Depth Analysis of the Human Study

While the above main results highlight the promise of LLMs in generating novel research ideas, there are some additional nuances.
In this section, we move beyond the statistical comparisons and dive into other aspects of our collected data.
Specifically, we focus on the quality of human ideas, reviewer preferences, and the extent of reviewer agreement.

### 6.1 Human Experts May Not Be Giving Their Best Ideas

We first investigate whether human experts are submitting their best ideas to us.
We did a post-study survey to understand how idea-writing participants came up with their ideas. Out of the 49 participants, 37 of them came up with the idea on the spot, while the other 12 already had the idea before the study. Furthermore, we asked the survey question: “*How does this idea compare to your past research ideas (ideas that you actually worked on)? Please answer with a percentile. E.g., this idea is one of my top 10% ideas.*” Our participants indicated that on average their submitted ideas are about the top 43% of all their past ideas. This implies that our collected ideas are likely the median-level ideas from these expert researchers, which is reasonable given that most of them came up with the idea within the 10-day time constraint of the task.

### 6.2 Reviewers Tend to Focus More on Novelty and Excitement

To gain a deeper understanding of the dynamics between the different metrics in the review process, we explore whether reviewers focus on specific aspects when evaluating the ideas.
We compute the pairwise correlation between different metrics in Table 10. The overall score mostly correlates with the novelty score ($r=0.725$) and excitement score ($r=0.854$), while having almost no correlation ($r<0.1$) with the feasibility score. This implies that reviewers might be paying more attention to the novelty and excitement aspects of the ideas when they are reviewing.

**Table 10: Pairwise correlation between different metrics (symmetric matrix).**
|  | Overall | Novelty | Excitement | Feasibility | Effectiveness |
| --- | --- | --- | --- | --- | --- |
| Overall | – | 0.725 | 0.854 | 0.097 | 0.642 |
| Novelty | 0.725 | – | 0.719 | -0.073 | 0.357 |
| Excitement | 0.854 | 0.719 | – | -0.031 | 0.565 |
| Feasibility | 0.097 | -0.073 | -0.031 | – | 0.251 |
| Effectiveness | 0.642 | 0.357 | 0.565 | 0.251 | – |

### 6.3 Reviewing Ideas is Inherently Subjective

Finally, we acknowledge that reviewing is inherently subjective, and reviewing based on ideas rather than executed papers might be even more subjective. We investigate this using inter-reviewer agreement.
Specifically, we randomly split reviewers of each paper into half, use one half to rank the top and bottom 25% of all ideas, and then measure agreement with the held-out set of reviewers. (^11^1111This metric follows the balanced accuracy metric as used in Lu et al. () and avoids the limitations of other agreement metrics like Krippendorff’s alpha, which require overlapping reviews and would result in a sparse matrix due to the non-overlapping nature of our reviewer assignments. We do the random splitting 20 times and report the average to reduce variances.)
As shown in the first block of Table 11, reviewers have a relatively low agreement ($56.1\%$) despite the fact that we have provided detailed explanations for each metric in our review form.
As a baseline comparison, the NeurIPS 2021 reviewer consistency experiment found $66.0\%$ accuracy using this reviewer agreement metric in the balanced setting Beygelzimer et al. (), Lu et al. ().
We also computed the reviewer agreement using the same metric on the 1.2K ICLR 2024 submissions related to language models, which has a balanced accuracy of $71.9\%$.
While our reviewer agreement is higher than random ($50\%$), it is generally lower than conference reviewing, most likely due to the higher subjectivity involved when evaluating ideas without seeing the actual experiment results.

## 7 Limitations of LLMs

With our findings from the human study in mind, we now turn to LLM performance to provide insights that could inform future methods for improving idea generation systems.
Our ideation agent is motivated by two potential strengths of LLMs: their ability to scale by generating a vast number of ideas - far more than any human could - and the possibility of filtering these ideas to extract the best ones from the large pool.
In theory, this approach could lead to high-quality ideas by leveraging inference scaling. However, we present empirical evidence that this naive assumption about scaling idea generation has significant limitations.

### 7.1 LLMs Lack Diversity in Idea Generation

Figure: Figure 4: Measuring duplication of AI-generated ideas: the left figure plots the percentage of non-duplicate ideas in each new bucket of generated ideas; the right figure plots the accumulated non-duplicate ideas as the agent keeps generating new ideas. All data points are averaged across all topics.
Refer to caption: x4.png

We adopted an over-generate and rank paradigm in idea generation. This raises the question: is there an upper limit to how many new ideas LLMs can generate? To answer this question, we take a closer look at 4000 generated seed ideas for each topic.

We encode all raw ideas with all-MiniLM-L6-v2 from Sentence-Transformers. For each idea, we compute its cosine similarity with all previously generated ideas on the same topic.
We consider an idea as a duplicate if it has a similarity of above 0.8 with any of the previously generated ideas.
In Figure 4, we show that as the agent keeps generating new batches of ideas, the percentage of non-duplicates in newly generated batches keeps decreasing, and the accumulated non-duplicate ideas eventually plateau. In fact, out of the 4000 generated seed ideas, there are only 200 non-duplicate unique ideas.
This sets a bottleneck on our inference-time scaling since increasing the number of generated ideas simply leads to repeating duplicate ideas.

### 7.2 LLMs Cannot Evaluate Ideas Reliably

**Table 11: Review score consistency among human reviewers (first block) and between humans and AI (second block).**
|  | Consistency |
| --- | --- |
| Random | 50.0 |
| NeurIPS’21 | 66.0 |
| ICLR’24 | 71.9 |
| Ours | 56.1 |
| GPT-4o Direct | 50.0 |
| GPT-4o Pairwise | 45.0 |
| Claude-3.5 Direct | 51.7 |
| Claude-3.5 Pairwise | 53.3 |
| “AI Scientist” Reviewer | 43.3 |

Most prior works have adopted *LLM-as-a-judge* for evaluating research ideas Lu et al. () motivated by the observation that LLMs can have a higher agreement with human evaluators than the inter-human agreement. However, we offer some empirical evidence that LLMs cannot evaluate ideas reliably yet.

Concretely, we use the average review score of each idea to rank the top and bottom $25\%$ of all our collected human and AI ideas,
and use this to benchmark various LLM evaluators. Specifically, we obtain the LLM predicted scores of all ideas and set the median score as the threshold to measure their accuracy on our balanced idea ranking data.

In the second block of Table 11, we compare several different LLM evaluators: 1) directly giving the review criteria and prompting for a final score Yang et al. (), Li et al. (), Baek et al. (); 2) our pairwise ranker as described in Section 3.3; and 3) the “AI Scientist” reviewer agent Lu et al. (). All of these LLM evaluators have a lower agreement than our expert reviewers’ scores. Even the best LLM evaluator — our own Claude-3.5 pairwise ranker — only achieves an accuracy of 53.3%, lower than our inter-reviewer consistency of 56.1%.

Even if AI-human agreement eventually matches or exceeds human-human agreement, simply meeting this baseline does not imply that AI-as-a-reviewer is meaningful, since we may be trading variance for bias, where AI reviewers are more consistent but rely on spurious correlations Durmus et al. (). Our findings in Table 11 are consistent with these brittleness concerns, as we find a significant drop in AI-human agreement scores under our study compared to the original studies.

Finally, even though Claude-3.5 pairwise agreements may seem close to human agreement, many other pieces of evidence throughout the paper leads us to be cautious about the use of LLM-as-a-judge in such a complex and subjective task. These include our findings on the significant discrepancy between the agent’s top-ranked ideas and the human expert’s top-ranked ideas (Appendix I) and how the AI Ideas + Human Rerank condition tends to score higher than the AI Ideas condition on all metrics in Section 5.
These limitations of LLM auto-evaluation not only constrain the effectiveness of our over-generate-and-rank paradigm for idea generation but also raise concerns about trusting conclusions that are based primarily on LLM evaluators.

## 8 Qualitative Analysis and Examples

In this section, we offer some qualitative analysis of human- and AI-generated ideas based on our collected reviews and present four pairs of randomly sampled human and AI ideas as case studies.

### 8.1 Analysis of Free-Text Reviews

Following recent practices of using LLMs to extract patterns from text corpora Zhong et al. (, ), we use Claude-3.5 to extract and cluster the main points from all reviews. We then manually verified and labeled each cluster.

Many reviews reinforce our quantitative finding that AI ideas tend to be more novel. For example, reviewers noted: “The idea of […] is quite novel in an in-context learning setting.”, “The idea of exploring […] using an LLM-based iterative approach is novel.”, “The idea of […] when constructing prompts to improve cross-lingual transfer is one that I have not heard of before.”, “I like the idea to […], and think it will be helpful for other researchers in the community.”, “Combining […] is a unique way of attempting to preserve the gist of the information while likely losing specific identifiers.”, and “Safeguarding using […] is clearly novel. Similar ideas have not been seen in the related work.”.

Next, we summarize some common failure modes of AI ideas:

- 1.
Being too vague on implementation details. For example, one reviewer noted: “I’m not super clear on the details of this lattice and how the model will be prompted, so I’m not super sure how well the model will complete these subtasks and how well-suited this particular structure is to completing the overall task.” and another reviewer noted: “"For analyzing the effectiveness of the method, the proposal only provides a very ad-hoc + hand-wavey suggestion to compare responses across predefined questions.” In another case, the AI idea is criticized for not considering practical implementation details: “I think in each of the steps, there is something hard to execute. For example, in step Constellation Formation, how do we do the weighted sum?” Similarly, other reviews noted: “It’s unclear how CLIP is connected to the language model and how training a CLIP model would enable the LM to understand images.”, and “There’s no mentioning on how to prompt the model to generate defensive strategies and refine the model’s responses using these strategies.”
Such vagueness often makes it difficult for reviewers to make confident judgments: “Because this idea is too general and vague, I can’t really answer the previous question. An idea needs a certain level of details to be determined if it fits for a conference/journal but this one misses them.”
- 2.
Misuse of datasets. For example: “I’m not sure about the datasets picked. StereoSet is not a QA dataset; it simply contains statements. Also, I don’t understand why Dialogue NLI responses require empathy.”, “I’m concerned the datasets proposed are the right test cases for security of the code (since they are really just ML/programming problems, not system-level programming).”, and “the choice of datasets might not be the best to show the effect of incorporating multiple perspectives, especially TruthfulQA and ScienceQA, which seems to have a single correct interpretation and answer.”
In another example, the benchmark datasets chosen are considered too easy by the reviewer: “none of the chosen datasets (MATH, GSM8K, and MMLU) uses complicated math concepts”.
- 3.
Missing or inappropriate baselines. For example: “The proposed method needs to be compared to simply asking the model to think of one (or several) facts about the question before answering using more turns. This could be an additional baseline to verify the scoring process is meaningful.” and “Although the proposal includes some baselines that should be compared to, it does not mention some methods which seem to do quite well with LLMs.” Sometimes, “the chosen baselines may not be suitable”, for example, because they are not directly comparable with the proposed method.
- 4.
Making unrealistic assumptions. For example: “The assumption that model can (mostly) accurately flag its own hallucinations is quite tricky.”, “there is a presupposed assumption that hallucinations in LLMs are ungrounded and independent of the data they are trained on, which is generally not considered true”, “The big issue for the effectiveness of the proposed method is that, it asserts very strong assumptions on downstream tasks, such as there must exist only two extremes.”, “Some assumptions (e.g., […]) are unlikely to be true in practice, especially when low-resource languages and less represented cultures are included in the study.”, and “A major assumption in this approach is that the model is able to […]. However, […]”.
- 5.
Being too resource-demanding. Despite the fact that we explicitly prompted the agent to consider feasibility when generating ideas, some of the generated ideas are still too resource-demanding. For example, one reviewer noted: “The biggest issue to feasibility I see is that the project calls for fine-tuning BLOOM (See step 5). BLOOM has 176B parameters so it’s going to take quite a lot of GPUs to fine-tune. From a systems perspective, I see this as causing delays.” In some other cases, manual data annotation is being criticized for feasibility: “The bottleneck seems to be the dataset collection process if there are no existing datasets that fit the requirements of the paper.”, and “the manual evaluation by native speakers or cultural experts could be time-consuming and resource-intensive”.
- 6.
Not well-motivated. For example: “Not well-motivated and there is not a clear intuition that this work can work to increase the factuality.”, “And in general the method is not well-motivated and needs reasons why retrieving from model itself is meaningful by use cases or specific tasks.”, and “The idea simply doesn’t make sense to me. Given current LLMs’ ability, I’m pretty sure they can simply recite code like inserting data to a binary search tree.”
- 7.
Not adequately following existing best practices. For example: “The proposal does not seem to include awareness of what has been previously tried, or more strategic ways to evaluate success/failures…”

We contrast these with some of the unique strengths and weaknesses of human ideas:

- 1.
Human ideas are generally more grounded in existing research and practical considerations, but may be less innovative. For example, these ideas might be applying existing techniques to new problems: “Multilinguality as a debiasing method has already been considered in the literature, although not necessarily in the prompt engineering framework.” Sometimes people apply incremental changes to existing techniques: “The overall idea shares quite a similar idea with program-of-thought (PoT). The only difference is that there is an additional step where an LLM is prompted to decide whether to use code or not.” Some ideas try to combine existing techniques: “Query decomposition and RAG separately are well studied, if there is no existing work that combines both (which I’m not aware of), then it’s reasonably novel.” As some reviewers noted, human ideas tend to build on known intuitions and results: “There are already existing works on using available lexicons to improve the translation capabilities of LLMs in general.”
- 2.
Human ideas tend to be more focused on common problems or datasets in the field. For example: “The problem of models not handling negation properly is a very common problem, especially among propriety LMs such as claude-3-5-sonnet.”, “The data exist. This project mainly entails plugging in these datasets to a prompt template and finetuning for a bit. There is little left unspecified, and it should be quite simple to execute on.”, “I haven’t found any work using this idea to solve this specific problem, but […] is definitely not new.”, and “While existing works have explored the problem of calibration in long-form answers (e.g. […]), the specific method for calibration is different.”
- 3.
Human ideas sometimes prioritize feasibility and effectiveness rather than novelty and excitement. For example, reviewers noted: “I don’t think this will be a groundbreaking finding, but it will probably work.” and “while the idea is promising and could lead to significant improvements, it may not be groundbreaking enough to be considered transformative or worthy of a best paper award”.

### 8.2 Randomly Sampled Human and AI Ideas with Reviews

We randomly sample four pairs of ideas from different topics to ground our numerical results with actual examples.
In each pair, there is one AI idea and one human idea. To save space, we include the full project proposal of each idea along with the full set of reviews in the Appendix, but we list their titles, topics, and average scores here for quick reference (we reveal whether each idea is AI-generated or human-written in Appendix X):

- 1.
Modular Calibration for Long-form Answers: Appendix P
Topic: Uncertainty; Average Overall Score: 5.5
- 2.
Semantic Resonance Uncertainty Quantification: Calibrating LLM Confidence through Multi-Path Reasoning: Appendix Q
Topic: Uncertainty; Average Overall Score: 6
- 3.
Translation with LLMs through Prompting with Long-Form Context: Appendix R
Topic: Multilingual; Average Overall Score: 4
- 4.
Linguistic Pivot Constellation: Enhancing Cross-Lingual Transfer for Low-Resource Languages and Dialects: Appendix S
Topic: Multilingual; Average Overall Score: 6.7
- 5.
LLM Directed Retrieval Querying for Improving Factuality: Appendix T
Topic: Factuality; Average Overall Score: 4.7
- 6.
Semantic Divergence Minimization: Reducing Hallucinations in Large Language Models through Iterative Concept Grounding: Appendix U
Topic: Factuality; Average Overall Score: 3.3
- 7.
Autoprompting: Generate Diverse Few-shot Examples for Any Application: Appendix V
Topic: Coding; Average Overall Score: 5
- 8.
Temporal Dependency Unfolding: Improving Code Generation for Complex Stateful Systems: Appendix W
Topic: Coding; Average Overall Score: 6.7

## 9 Related Work

Research idea generation and execution.
Several prior works explored methods to improve idea generation, such as iterative novelty boosting Wang et al. (), multi-agent collaboration Baek et al. (), and multi-module retrieval and revision Yang et al. ().
While some of them share similar components as our ideation agent, these works focus on improving the idea generation methods over vanilla prompting baselines, without comparisons to any human expert baselines.
Beyond ideation, another line of work uses LLMs for executing experiments by generating code given the research problems Huang et al. (), Tian et al. (), or combining idea generation with code generation to directly implement AI-generated ideas Lu et al. (), Li et al. ().
These works either use automatic evaluation on a pre-defined set of problems and benchmarks, setting a constrained problem space; or rely on proxy metrics like LLM evaluators, which are often unreliable.

LLM for other research-related tasks.
LLMs have also been used for several other research-related tasks, such as generating code to perform data-driven discovery Majumder et al. (), Hu et al. (), Guo et al. (), Gu et al. (), Ifargan et al. (), automatic review generation D’Arcy et al. (), Liang et al. (), related work curation Kang and Xiong (), Ajith et al. (), Press et al. (), Lehr et al. (), experiment outcome prediction Lehr et al. (), Zhang et al. (), Manning et al. (), Hewitt et al. (), and future work recommendation Zhang et al. ().
Unlike these works, we tackle the more creative and open-ended task of research ideation.

Computational creativity.
Our work also connects to the line of work on examining AI’s novelty and diversity in creative tasks.
Chakrabarty et al. () found that AI writings are less creative than professional writers, while we show LLM-generated ideas can be more novel than experts on the task of research ideation.
Another line of work found that LLM generations lack collective diversity Zhou et al. (), Anderson et al. (), which matches our findings on idea generation.
Lastly, several other works conducted human evaluation to study the impact of AI exposure or human-AI collaboration on novelty and diversity Padmakumar and He (), Ashkinaze et al. (), Liu et al. () with mixed conclusions. While we also conduct a human evaluation of idea novelty, we focus on the human-AI comparison on the challenging task of research ideation with expert participants.

## 10 Discussion

To summarize, we compared research ideas generated by our AI agent with ideas written by expert researchers, and observed the robust finding that expert reviewers rate AI ideas as statistically more novel than expert ideas. In this section, we discuss some high-level questions readers might have and suggest some ways to address them.

Question 1: Do these collected expert ideas represent their best ideas?
One might argue that these ideas submitted by our idea-writing participants might not represent their best ideas as we discussed in Section 6.1, since most of them came up with the idea on the spot within a short period.
In order to address this concern, we have designed an experiment where we will compare AI ideas with papers accepted at top-tier AI conferences.
To avoid any possible contamination, we target the upcoming EMNLP 2024 conference, which will release the accepted papers in October 2024.
We have generated AI ideas with our agent on 23 topics from the EMNLP Call For Papers page in July 2024 and cached them.
We pre-registered our analysis plan which also includes the link to the cached ideas. (^12^1212[https://osf.io/z6qa4](https://osf.io/z6qa4))
Apart from comparing the quality of these ideas, we will also compute the overlap between AI-generated ideas and accepted papers on the same topics.

Question 2: Are evaluations based solely on ideas subjective?
In this current study, we focused solely on evaluating the ideas themselves. Ideas that sound novel and exciting might not necessarily turn into successful projects, and our results indeed indicated some feasibility trade-offs of AI ideas.
We view the current study as a preliminary evaluation of AI-generated ideas. In the next phase, we will recruit researchers to execute some AI and human-generated ideas into full projects. This will enable reviewers to assess the complete experimental outcomes, providing a more reliable basis for evaluation. Furthermore, it will allow us to analyze whether our initial idea evaluations align with the assessments of the actual project outcomes.

Question 3: Why do you focus only on prompting-based research in NLP?
The scope of our study is limited to prompting research ideas within NLP. We chose this design to facilitate the next phase of our execution experiment, where we prefer research ideas that are less resource-demanding and can be executed relatively quickly.
We believe that the evaluation protocols we established should be applicable to other research domains as well, although the conclusions could be different depending on the research fields.
Future work should consider extending such human study to other research domains and it would be interesting to compare how the conclusions differ.

Question 4: Can you automate idea execution as well?
It is tempting to envision an end-to-end automated research pipeline where AI agents can implement AI-generated ideas to directly evaluate their effectiveness.
Apart from speeding up scientific discovery, one could also imagine using such execution agents to automatically verify experiment results in existing papers or new submissions.
We have also explored building an LLM agent to generate code to implement the generated ideas.
Specifically, we provide a template codebase that consists of: (1) loading datasets from Huggingface or generating synthetic test examples; (2) implementing baseline methods; (3) implementing the proposed method; (3) loading or implementing the evaluation metrics; (4) running experiments on the testset with the baselines and the proposed method, so that
the output of the agent will be a report of the baseline performance as well as the proposed method’s performance.
While this agent can generate code that compiles and executes, we find that the automated experiments can be misleading because the agent often skips or modifies steps in the baselines or proposed methods. In some cases, the metric functions are also not correctly defined.
This highlights the core challenge: just comparing the final experiment results is not enough; we have to verify the faithfulness of the implementations as well.
Performing such implementation verification is not a trivial task, and we leave it to future work.
We provide detailed description of our idea execution agent in Appendix Y.

## 11 Ethical Considerations

Publication Policy.
The growing use of AI to generate research ideas raises serious concerns about the potential abuse of these technologies by students or researchers who may flood academic conferences with low-quality or poorly thought-out submissions. The availability of LLM-generated content could lead to a decline in the overall quality of academic discourse, as some individuals might take a lazy approach, relying on AI to both generate ideas and review submissions. This would undermine the credibility and integrity of the review process.
The risks are real. Without proper oversight, we could see a deluge of submissions that lack depth or intellectual merit. To prevent this, it is essential to hold researchers accountable for the outputs generated through AI tools. Rigorous standards must be applied equally to both AI-assisted and human-generated research to ensure that the use of LLMs does not result in misleading, superficial, or unethical academic contributions.

Intellectual Credit.
The use of LLMs to generate research ideas introduces significant ambiguity around the concept of intellectual credit. Traditional frameworks for attributing credit in research, based on human authorship and contribution, become less clear when AI plays a significant role in idea generation. Questions arise around how to distribute credit between the developers of the LLM, the researchers who designed the frameworks for its use, and the researchers who integrate AI-generated ideas into their work.
Furthermore, it becomes increasingly difficult to trace the origins of AI-generated contributions, especially when they draw from vast datasets composed of numerous sources. This complexity calls for a broader rethinking of how intellectual credit is assigned in AI-driven research.
While a complete overhaul of legal and academic norms is beyond the scope of this project, we advocate for the adoption of transparent documentation practices. Researchers should clearly disclose the role AI played in the idea generation process, specifying which models, data sources, and frameworks were used, and outlining the level of human involvement. This could ensure that the credit distribution in AI-supported research is as transparent and fair as possible.

Potential for Misuse.
AI-generated research ideas, especially those that introduce novel concepts, have the potential to be misused in ways that could lead to harmful or destabilizing outcomes. For instance, ideation agents could be leveraged to generate adversarial attack strategies or other unethical applications. This concern aligns with broader arguments from those focused on existential risk (X-risk), who argue that AI-driven innovation could be a primary route to destabilizing the status quo, posing risks at a societal or even global level.
Our stance is that such discussions on safety should be evidence-based to the extent that it is possible, and careful evaluation work is an important component of keeping these discussions grounded in actual, measured capabilities of these systems. We advocate for continued safety research specifically targeting these types of concerns—such as the development of Reinforcement Learning from Human Feedback (RLHF) systems or anti-jailbreak mechanisms for research ideation agents. Additionally, we believe it would be meaningful to create safety benchmarks that assess the ethical and safe application of AI-generated ideas.

Idea Homogenization.
Our analysis showed that current LLMs lack diversity in idea generation.
This raises important concerns that wide adoption of LLMs
can result in idea homogenization, where the generated ideas only reflect a narrow set of perspectives or have systematic biases.
Over time, this could lead to a reduction in the richness and diversity of research outputs globally.
Future work should develop ways to either improve LLMs themselves or refine our idea generation methods to promote idea diversity.
It’s also important to note that our evaluation primarily assesses the quality of the typical ideas being generated, and may not fully capture the long tail of unique or novel ideas that would be truly transformative.

Impact on Human Researchers.
The integration of AI into research idea generation introduces a complex sociotechnical challenge, as research is fundamentally a community-driven, collaborative effort. By introducing AI, particularly LLMs, into this social system, we risk unforeseen consequences.
Overreliance on AI could lead to a decline in original human thought, while the increasing use of LLMs for ideation might reduce opportunities for human collaboration, which is essential for refining and expanding ideas.
To mitigate these risks, future works should explore new forms of human-AI collaboration, and our results on human reranking of AI ideas show that even naive human-AI collaboration approaches can be effective. Beyond reranking, humans can play a critical role in the ideation process by providing intermediate feedback, taking AI-generated ideas as inspiration for further development, and bringing their unique expertise into the process. Understanding how to integrate LLMs into this collaborative process without disrupting the social fabric of research will be an important ongoing problem, requiring careful consideration of the broader sociotechnical implications.

## Positionality Statement

We disclose the authors’ anticipated outcomes of the human study before the experiment was conducted to be transparent about experimenter biases.
Among the three authors, Tatsu and Diyi were expecting a null result from the study while Chenglei expected AI to be better than humans.

## Acknowledgement

We thank all participants who wrote and reviewed ideas for us.
Many of them also provided insightful feedback on various aspects of this study.
This project would not have been possible without their support.
To ensure the integrity and fairness of phase II of our study, we leave our participants anonymous but will update this manuscript with a detailed acknowledgment of all participants in the project’s final report.

We thank Rose Wang, Dora Zhao, Irena Gao, Isabel Gallegos, Ken Liu, Aryaman Arora, Harshit Joshi, Shi Feng, Tianyu Gao, Xinran Zhao, Yangjun Ruan, Xi Ye, Mert Yuksekgonul, and members of Tatsu Lab and SALT Lab for their helpful feedback on the early version of this draft.

We thank our undergraduate intern Isha Goswami and faculty administrator Eric Alejandro Pineda for assisting with review data collection and financial logistics.

This work was supported by gifts from Open Philanthropy, Tianqiao and Chrissy Chen Institute, Meta, IBM, and Amazon, and grants from ONR, NSF IIS-2247357, and CNS-2308994.

## Appendix A List of Research Topics

We selected the following list of research topics for our research ideation task:

- 1.
Bias: novel prompting methods to reduce social biases and stereotypes of large language models
- 2.
Coding: novel prompting methods for large language models to improve code generation
- 3.
Safety: novel prompting methods to improve large language models’ robustness against adversarial attacks or improve their security or privacy
- 4.
Multilingual: novel prompting methods to improve large language models’ performance on multilingual tasks or low-resource languages and vernacular languages
- 5.
Factuality: novel prompting methods that can improve factuality and reduce hallucination of large language models
- 6.
Math: novel prompting methods for large language models to improve mathematical problem solving
- 7.
Uncertainty: novel prompting methods that can better quantify uncertainty or calibrate the confidence of large language models

We use these topics descriptions to elicit ideas from both human participants and our LLM agent.

## Appendix B Project Proposal Template

We give the following project proposal template to both the AI agent and human idea writers.

1. Title:
A concise statement of the main research question to be used as the paper title.

2. Problem Statement:
Clearly define the problem your research intends to address. Explain clearly why this problem is interesting and important.

3. Motivation:
Explain why existing methods are not good enough to solve the problem, and explain the inspiration behind the new proposed method. You should also motivate why the proposed method would work better than existing baselines on the problem.

4. Proposed Method:
Explain how the proposed method works, describe all the essential steps.

5. Step-by-Step Experiment Plan:
Break down every single step of the experiments, make sure every step is executable. Cover all essential details such as the datasets, models, and metrics to be used. If the project involves prompting, give some example prompts for each step.

6. Test Case Examples:
Give at least two concrete examples. The first example should show how the baseline method fails on the test case. If there are multiple baselines, give examples for all of them. The second example should show how the proposed method succeeds on the test case. For each test case, include the input (test example and the full prompt) and the expected output. You should also provide an explanation for why the outputs from the proposed prompt are better. If the proposed method has multiple steps, break them down into intermediate steps.

7. Fallback Plan:
Propose some alternative plans for what should the students do if the proposed method doesn’t manage to satisfy the success criteria. For example, you can suggest additional analysis to help debug why the proposed method didn’t work, which could inform alternative new methods, or just turn the project into an analysis paper instead by offering some interesting ablation and insights.

## Appendix C Project Proposal Demo Example

We present a manually written demonstration example used for project proposal generation. The example is summarized from an existing paper Dhuliawala et al. (). This same example is given to both the AI agent as well as the idea-writing experts.

1. Title:
Chain-of-Verification Reduces Hallucination in Large Language Models

2. Problem Statement:
Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models.

3. Motivation:
A majority of the methods for reducing hallucination can be divided into roughly three categories: training-time correction, generation-time correction, and via augmentation (tool-use). We want to take a simpler approach that fully leverages the power of LLM itself. Our key motivation is that large language models, when suitably prompted, can both generate and execute a plan of how to verify themselves in order to check their own work, and finally incorporate this analysis into an improved response.

4. Proposed Method:
Our overall process, which we call Chain-of-Verification (CoVe), thus performs four core steps:

- (1)
Generate Baseline Response: Given a query, generate the response using the LLM.
- (2)
Plan Verifications: Given both query and baseline response, generate a list of verification questions that could help to self-analyze if there are any mistakes in the original response.
- (3)
Execute Verifications: Answer each verification question in turn, and hence check the answer against the original response to check for inconsistencies or mistakes.
- (4)
Generate Final Verified Response: Given the discovered inconsistencies (if any), generate a revised response incorporating the verification results.

Each of these steps is performed by prompting the same LLM in different ways to obtain the desired response.

5. Step-by-Step Experiment Plan:

- 1:
Gather Datasets: We choose datasets that evaluate factual correctness, including the MultiSpanQA dataset on closed-book QA and the FactScore dataset on generating biographies.
- 2:
Construct Prompts: For the baseline, we use direct prompting where, given a query, we generate left-to-right as usual using the LLM, with no special tricks. Given that such baseline generations are typically prone to hallucination, CoVe attempts to identify these hallucinations and correct them in the following steps:
(1)
Plan Verifications: Conditioned on the original query and the baseline response, the model is prompted to generate a series of verification questions that test the factual claims in the original baseline response.
(2)
Execute Verifications: Given the planned verification questions, the next step is to answer them in order to assess if any hallucinations exist. The planning prompt conditions on the baseline response in the first step. The verification questions generated from planning are answered in the second step, where crucially the context given to the LLM prompt only contains the questions and not the original baseline response, hence preventing the LLM from repeating those answers directly.
(3)
Generate Final Verified Response: Finally, the improved response that takes verification into account is generated. This is executed by a final few-shot prompt where the context takes into account all of the previous reasoning steps, the baseline response, and verification question-answer pairs, so that the corrections can take place.
- 3:
Select Models: We test GPT-3.5 (Text-Davinci-003) and GPT-4 from the OpenAI API, as well as the open-source LLaMA-2-70B-chat.
- 4:
Get Results: Get answer predictions from the models on these datasets with both the baselines and the proposed method.
- 5:
Analyze Results: Compare whether the new method improves the performance of LLMs in these tasks as compared to the baselines.

6. Test Case Examples:

- •
Baseline Prompt Input (Direct Prompting): Name some politicians who were born in NY, New York.
- •
Baseline Prompt Expected Output (Direct Prompting): Here are some politicians who were born in NY, New York:
1. Hillary Clinton - former secretary of state and former Democratic presidential nominee.
2. Donald Trump - former president of the United States.
3. Michael Bloomberg - former Mayor of New York City and former Democratic presidential candidate.
- •
Proposed Prompt Input (Chain-of-Verification Prompting; Step 1: Plan Verifications): Generate a set of verification questions to verify each answer.
- •
Proposed Prompt Expected Output (Chain-of-Verification Prompting; Step 1: Plan Verifications):
–
Where was Hillary Clinton born?
–
Where was Donald Trump born?
–
Where was Michael Bloomberg born?
- •
Proposed Prompt Input (Chain-of-Verification Prompting; Step 2: Execute Verifications): Answer each of the verification questions.
- •
Proposed Prompt Expected Output (Chain-of-Verification Prompting; Step 2: Execute Verifications):
1.
Hillary Clinton was born in Chicago, Illinois, United States on October 26, 1947.
2.
Donald Trump was born on June 14, 1946, in Queens, New York City, New York, United States.
3.
Michael Bloomberg was born on February 14, 1942, in Boston, Massachusetts, United States.
- •
Proposed Prompt Input (Chain-of-Verification Prompting; Step 3: Final Response): (Prepend the original question and the baseline response and the verification QA pairs) Given the verification answers, generate a revised response incorporating the verification results.
- •
Proposed Prompt Expected Output (Chain-of-Verification Prompting; Step 3: Final Response): Here are some politicians who were born in NY, New York:
1. Donald Trump - former president of the United States.
2. Alexandria Ocasio-Cortez - Democratic member of the U.S. House of Representatives.
- •
Explanation: Given a user query, a large language model with direct prompting generates a baseline response that may contain inaccuracies, e.g., factual hallucinations. To improve this, Chain-of-Verification first generates a plan of a set of verification questions to ask, and then executes that plan by answering them and hence checking for agreement. We find that individual verification questions are typically answered with higher accuracy than the original accuracy of the facts in the original longform generation. Finally, the revised response takes into account the verifications.

7. Fallback Plan:
If the proposed method does not help as compared to the baseline, analyze each step of the CoVe process to see if the verification questions are relevant, if the answers to the verification questions are correct, and whether the generated final verified response is indeed improved over the baseline response by considering the verification QA pairs. This can help us debug the proposed method or turn this into interesting analysis on the model’s ability to verify and correct its own responses.

## Appendix D Style Standardization Prompt

## Appendix E Idea Review Form

We use the following review form to elicit reviews from all expert reviewers. Reviewers have one week of time to finish each review.

1. Name

2. Institution

3. Email

4. Consent

5. Honor Code: I confirm that I will not use ChatGPT, Claude, Gemini, or any other AI tools when writing my reviews.

6. Familiarity: Before reviewing the idea, please indicate how familiar you are with the given topic on a scale of 1 - 5 (this is just for us to understand potential confounders).

- 1.
You have never read about this topic before
- 2.
You have read at least one paper on this topic
- 3.
You have read multiple papers on this topic but have not published any paper on it
- 4.
You have co-authored at least one paper on this topic
- 5.
You have co-authored multiple papers on this topic or have published at least one first-author paper on this topic

7. Experience: Have you reviewed for major NLP or AI conferences before (e.g., *ACL, COLING, NeurIPS, ICLR, ICML, AAAI)?

8. Full Research Idea Proposal

9. Novelty Score: Whether the idea is creative and different from existing works on the topic, and brings fresh insights. You are encouraged to search for related works online. You should consider all papers that appeared online prior to July 2024 as existing work when judging the novelty.

- 1.
Not novel at all - there are many existing ideas that are the same
- 2.
- 3.
Mostly not novel - you can find very similar ideas
- 4.
- 5.
Somewhat novel - there are differences from existing ideas but not enough to turn into a new paper
- 6.
Reasonably novel - there are some notable differences from existing ideas and probably enough to turn into a new paper
- 7.
- 8.
Clearly novel - major differences from all existing ideas
- 9.
- 10.
Very novel - very different from all existing ideas in a very interesting and clever way

10. Novelty Rationale: Short justification for your score. If you give a low score, you should specify similar related works. (Your rationale should be at least 2-3 sentences.)

11. Feasibility Score: How feasible it is to implement and execute this idea as a research project? Specifically, how feasible the idea is for a typical CS PhD student to execute within 1-2 months of time. You can assume that we have abundant OpenAI / Anthropic API access, but limited GPU compute.

- 1.
Impossible: the idea doesn’t make sense or the proposed experiments are flawed and cannot be implemented
- 2.
- 3.
Very challenging: there are flaws in the proposed method or experiments, or the experiments require compute/human resources beyond any academic lab
- 4.
- 5.
Moderately feasible: It can probably be executed within the given time frame but would require careful planning, efficient use of APIs or some advanced computational strategies to overcome the limited GPU resources, and would require some modifications to the original proposal to make it work
- 6.
Feasible: Can be executed within the given constraints with some reasonable planning
- 7.
- 8.
Highly Feasible: Straightforward to implement the idea and run all the experiments
- 9.
- 10.
Easy: The whole proposed project can be quickly executed within a few days without requiring advanced technical skills

12. Feasibility Rationale: Short justification for your score. If you give a low score, you should specify what parts are difficult to execute and why. (Your rationale should be at least 2-3 sentences.)

13. Expected Effectiveness Score: How likely the proposed idea is going to work well (e.g., better than existing baselines).

- 1.
Extremely Unlikely: The idea has major flaws and definitely won’t work well
- 2.
- 3.
Low Effectiveness: The idea might work in some special scenarios but you don’t expect it to work in general
- 4.
- 5.
Somewhat ineffective: There might be some chance that the proposed idea can work better than existing baselines but the improvement will be marginal or inconsistent
- 6.
Somewhat effective: There is a decent chance that the proposed idea can beat existing baselines by moderate margins on a few benchmarks
- 7.
- 8.
Probably Effective: The idea should offer some significant improvement over current methods on the relevant benchmarks
- 9.
- 10.
Definitely Effective: You are very confident that the proposed idea will outperform existing methods by significant margins on many benchmarks

14. Expected Effectiveness Rationale: Short justification for your score. (Your rationale should be at least 2-3 sentences.)

15. Excitement Score: How exciting and impactful this idea would be if executed as a full project. Would the idea change the field and be very influential.

- 1.
Poor: You cannot identify the contributions of this idea, or it’s not interesting at all and you would fight to have it rejected at any major AI conference
- 2.
- 3.
Mediocre: this idea makes marginal contributions and is very incremental
- 4.
- 5.
Leaning negative: it has interesting bits but overall not exciting enough
- 6.
Learning positive: exciting enough to be accepted at a major AI conference, but still has some weaknesses or somewhat incremental
- 7.
- 8.
Exciting: would deepen the community’s understanding or make major progress in this research direction
- 9.
- 10.
Transformative: would change the research field profoundly and worth a best paper award at major AI conferences

16. Excitement Rationale: Short justification for your score. (Your rationale should be at least 2-3 sentences.)

17. Overall Score: Overall score: Apart from the above, you should also give an overall score for the idea on a scale of 1 - 10 as defined below (Major AI conferences in the descriptions below refer to top-tier NLP/AI conferences such as *ACL, COLM, NeurIPS, ICLR, and ICML.):

- 1.
Critically flawed, trivial, or wrong, would be a waste of students’ time to work on it
- 2.
Strong rejection for major AI conferences
- 3.
Clear rejection for major AI conferences
- 4.
Ok but not good enough, rejection for major AI conferences
- 5.
Decent idea but has some weaknesses or not exciting enough, marginally below the acceptance threshold of major AI conferences
- 6.
Marginally above the acceptance threshold of major AI conferences
- 7.
Good idea, would be accepted by major AI conferences
- 8.
Top 50% of all published ideas on this topic at major AI conferences, clear accept
- 9.
Top 15% of all published ideas on this topic at major AI conferences, strong accept
- 10.
Top 5% of all published ideas on this topic at major AI conferences, will be a seminal paper

18. Overall Rationale: You should also provide a rationale for your overall score. (Your rationale should be at least 2-3 sentences.)

19. Confidence: Additionally, we ask for your confidence in your review on a scale of 1 to 5 defined as following:

- 1.
Your evaluation is an educated guess
- 2.
You are willing to defend the evaluation, but it is quite likely that you did not understand central parts of the paper
- 3.
You are fairly confident that the evaluation is correct
- 4.
You are confident but not absolutely certain that the evaluation is correct
- 5.
You are absolutely certain that the evaluation is correct and very familiar with the relevant literature

20. Time: How many minutes did you spend on this task?

## Appendix F Idea Generation Agent: Additional Implementation Details

#### Seed Idea Generation

Due to the max output length limit of the LLM API, we first generate a large number of shorter seed ideas.
We keep the seed ideas short so that we can explore more different ideas given the same output token budget.
We provide a demonstration example of the seed idea in Appendix G.
Then, we perform duplication and expand each remaining seed idea into a full project proposal following our standard template in Appendix B.

#### Retrieval Augmentation

We apply retrieval augmentation to the idea generation prompt in order to increase diversity in the idea generation. To maximize diversity, we apply retrieval augmentation half of the time when generating seed ideas, and we randomly select $k=10$ papers from the top 20 retrieved papers when applying retrieval augmentation.

#### Idea Filtering

After expanding seed ideas into full project proposals, we did some basic filtering to remove any project proposals that failed the novelty and feasibility checks:

- 1.
Novelty: We use the literature review module to retrieve the top 10 most relevant papers to the generated idea and ask the LLM to compare each of them to the generated idea. The idea will be filtered as long as any one of the retrieved papers is judged as equivalent.
- 2.
Feasibility: The idea will be filtered if it requires extensive manual labor or hardware resources beyond the capacity of a typical academic lab. The idea will also be filtered if it involves any inconsistency in the experimental setups or assumptions. For example, if the idea assumes only black-box API access of the LLMs, then it shouldn’t involve experiments that need internal weight access.

This filtered out about 1% of the generated project proposals.

## Appendix G Demonstration Example: Seed Idea Generation

We present a demonstration example used for seed idea generation. The example is summarized from an existing paper Dhuliawala et al. ().

Title:
Chain-of-Verification Prompting

Problem:
Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models.

Existing Methods:
A majority of the methods for reducing hallucination can be divided into roughly three categories: training-time correction; generation-time correction; and via augmentation (tool-use).

Motivation:
A key observation is that large language models, when suitably prompted, can both generate and execute a plan of how to verify themselves in order to check their own work, and finally incorporate this analysis into an improved response.

Proposed Method:
Our overall process, which we call Chain-of-Verification (CoVe), thus performs four core steps:

- (1)
Generate Baseline Response: Given a query, generate the response using the LLM.
- (2)
Plan Verifications: Given both query and baseline response, generate a list of verification questions that could help to self-analyze if there are any mistakes in the original response.
- (3)
Execute Verifications: Answer each verification question in turn, and hence check the answer against the original response to check for inconsistencies or mistakes.
- (4)
Generate Final Verified Response: Given the discovered inconsistencies (if any), generate a revised response incorporating the verification results.

Each of these steps is performed by prompting the same LLM in different ways to obtain the desired response.

Experiment Plan:
Compare with zero-shot prompting, Chain-of-Thought, and few-shot prompting on the MultiSpanQA dataset on closed-book QA and FactScore dataset on generating biographies.

## Appendix H Generated Seed Ideas and Their Nearest Neighbors

We present several randomly sampled generated seed ideas (see Appendix F for the definition of seed ideas) on the topic of “novel prompting methods that can better quantify uncertainty or calibrate the confidence of large language models”. For each idea, we show the most similar idea (nearest neighbor) based on the embedding similarity, along with the similarity score. In practice, we set a threshold threshold of 0.8 for determining whether two ideas are duplicates.

Idea 1:
Title: Adaptive Precision Boundary Probing
Problem: LLMs often provide uncertainty estimates that are either too coarse-grained or inappropriately precise, failing to adapt to the inherent ambiguity or precision requirements of different queries.
Existing Methods: Existing uncertainty quantification methods typically use fixed precision scales or calibration techniques that don’t adapt to the specific context and precision requirements of each query.
Motivation: Human experts adjust the precision of their uncertainty estimates based on the nature of the question and the available evidence. We can incorporate this adaptive approach to improve LLM uncertainty quantification.
Proposed Method: We introduce Adaptive Precision Boundary Probing (APBP), a dynamic prompting technique that iteratively refines the precision of uncertainty estimates. Given a query, APBP starts with a coarse-grained confidence interval. It then prompts the model to assess whether this interval is appropriately precise given the query’s context and the model’s knowledge. If the model determines that greater precision is warranted, APBP iteratively narrows the interval, prompting the model at each step to justify the increased precision. Conversely, if the model recognizes high ambiguity or limited knowledge, APBP widens the interval. Throughout this process, the model is asked to explicitly reason about the factors influencing the appropriate level of precision, such as the specificity of the query, the reliability of relevant knowledge, and potential sources of ambiguity. The final output is an uncertainty estimate with a precision level tailored to the specific query and the model’s knowledge state.
Experiment Plan: We will evaluate APBP on a diverse set of tasks with varying inherent precision requirements, including numerical estimation, date prediction, and open-ended text generation. We’ll compare APBP against fixed-precision uncertainty estimation methods, measuring both calibration accuracy and the appropriateness of precision levels as judged by human experts.

Nearest Neighbor of Idea 1:
Title: Contextual Confidence Oscillation
Problem: Current methods for quantifying uncertainty in large language models often fail to capture the dynamic nature of confidence across different contexts within a single query.
Existing Methods: Most existing approaches use static confidence scores or calibration techniques that don’t account for intra-query contextual shifts.
Motivation: Human confidence often fluctuates as we process different parts of a complex question or task. By mimicking this oscillation, we can potentially capture a more nuanced and accurate representation of model uncertainty.
Proposed Method: We propose Contextual Confidence Oscillation (CCO), a novel prompting technique that encourages the model to continuously re-evaluate and express its confidence as it processes a query. The prompt is structured as a series of checkpoints, where the model must pause its reasoning, reflect on its current confidence level, and explain any changes since the last checkpoint. This creates a confidence trajectory that can be analyzed for patterns, sudden drops, or gradual increases. Additionally, we introduce ’confidence disruptors’ - intentionally ambiguous or challenging sub-queries inserted at various points to test the model’s ability to recognize and express increased uncertainty when appropriate.
Experiment Plan: We will evaluate CCO against standard uncertainty quantification methods on a range of tasks, including multi-step reasoning problems, ambiguous queries, and long-form text analysis. We’ll measure not just overall accuracy of uncertainty estimates, but also the correlation between confidence oscillations and human-annotated difficulty levels of different parts of each query. We’ll also analyze how well the model’s expressed confidence trajectory aligns with its actual performance across different segments of complex tasks.

Similarity: 0.70

Idea 2:
Title: Quantum Superposition Confidence Prompting
Problem: Current LLMs struggle to accurately quantify uncertainty across multiple possible answers, often defaulting to overconfidence in a single response.
Existing Methods: Existing approaches typically involve single-path reasoning or limited branching, failing to capture the full spectrum of uncertainty.
Motivation: Inspired by quantum mechanics, where particles can exist in multiple states simultaneously, we propose a method that allows LLMs to consider multiple answer possibilities concurrently.
Proposed Method: We introduce Quantum Superposition Confidence Prompting (QSCP), where the LLM is instructed to generate multiple potential answers simultaneously, assigning confidence scores to each. The prompt encourages the model to ’exist in multiple states,’ exploring contradictory answers and their implications concurrently. For example: ’Imagine you are in a quantum superposition of multiple expert personas. Each persona will provide an answer to the following question, along with a confidence score (0-100%). Ensure the personas explore contradictory viewpoints. Question: [INSERT QUESTION]’. The LLM then generates responses from multiple personas, each with its own confidence score. The final uncertainty is derived from the distribution of these scores, providing a more nuanced understanding of the model’s confidence across possible answers.
Experiment Plan: Compare QSCP against standard prompting, chain-of-thought, and other uncertainty quantification methods on diverse question-answering datasets. Evaluate using metrics such as calibration error, Brier score, and a novel ’quantum uncertainty score’ that measures the spread and coherence of the generated answer superposition.

Nearest Neighbor of Idea 2:
Title: Quantum Superposition Prompting
Problem: Traditional methods for uncertainty quantification in large language models often fail to capture the full range of possible interpretations and outcomes, especially for queries with inherent ambiguity or multiple valid perspectives.
Existing Methods: Current approaches typically focus on generating a single response with an associated confidence score, or at best, a small set of discrete alternatives.
Motivation: Drawing inspiration from the principle of superposition in quantum mechanics, we propose a method to represent and reason about multiple possible outcomes simultaneously, providing a richer and more nuanced uncertainty quantification.
Proposed Method: We present Quantum Superposition Prompting (QSP), a novel framework for exploring and quantifying uncertainty in language model outputs. QSP begins by prompting the model to generate a ’superposition’ of possible interpretations or approaches to the given query. Each element in this superposition is assigned a complex amplitude, representing both its probability and its relationship to other elements. The model is then guided through a series of ’measurement’ prompts, designed to collapse this superposition along different bases of interpretation. These measurements yield probability distributions over outcomes, capturing different facets of uncertainty. QSP employs techniques inspired by quantum computing, such as interference and entanglement, to model how different interpretations interact and influence each other. The final uncertainty quantification is derived from the full set of measurements, providing a multi-dimensional representation of the model’s uncertainty that captures ambiguity, conflicting evidence, and the interdependence of different interpretations.
Experiment Plan: We will evaluate QSP on tasks that inherently involve multiple valid perspectives or ambiguous interpretations, such as ethical dilemmas, creative writing prompts, and open-ended analytical questions. Metrics will include the diversity and coherence of generated superpositions, the ability to capture human-judged ambiguities, and improvements in uncertainty calibration compared to classical methods.

Similarity: 0.77

Idea 3:
Title: Fractal Uncertainty Decomposition
Problem: LLMs often provide overly simplistic uncertainty estimates that fail to capture the hierarchical and nested nature of uncertainty in complex knowledge domains.
Existing Methods: Current uncertainty quantification methods typically produce flat, single-dimensional confidence scores that don’t reflect the multi-layered structure of knowledge and uncertainty.
Motivation: By recursively decomposing a query into sub-components and assessing uncertainty at multiple levels of granularity, we can construct a more comprehensive and structurally informed uncertainty estimate.
Proposed Method: We introduce Fractal Uncertainty Decomposition (FUD), a prompting technique that recursively breaks down a query into a hierarchical structure of sub-queries, assessing uncertainty at each level. Given an initial query, FUD prompts the model to identify key sub-components or aspects of the question. For each sub-component, the model provides an answer and a confidence estimate. If the confidence for a sub-component is below a certain threshold, FUD recursively applies the same decomposition process to that sub-component. This continues until either a maximum depth is reached or all sub-components have high confidence. The resulting structure is a tree of nested confidence estimates. FUD then aggregates these estimates bottom-up, using a combination of statistical methods and prompted meta-analysis by the model. The final output is both an overall uncertainty estimate and a detailed map of the uncertainty structure, showing how confidence varies across different aspects and levels of the query.
Experiment Plan: We will evaluate FUD on complex, multi-faceted tasks such as scientific explanation, historical analysis, and technical troubleshooting. We will compare its performance to flat confidence estimation methods and other hierarchical approaches. Evaluation metrics will include traditional calibration measures, as well as new metrics designed to assess the quality and informativeness of the uncertainty decomposition. We will also conduct case studies to demonstrate how FUD can provide more actionable and interpretable uncertainty information in real-world scenarios.

Nearest Neighbor of Idea 3:
Title: Semantic Fractal Decomposition
Problem: Current uncertainty quantification methods for large language models often fail to capture the hierarchical and self-similar nature of conceptual understanding, leading to inconsistent confidence estimates across different levels of abstraction.
Existing Methods: Existing approaches typically focus on flat, single-level uncertainty estimates or simple hierarchical decompositions that don’t fully capture the complex, nested nature of semantic understanding.
Motivation: Drawing inspiration from fractal geometry, where patterns repeat at different scales, we propose a method that recursively decomposes concepts and queries into self-similar sub-components, allowing for a more nuanced and scale-invariant approach to uncertainty quantification.
Proposed Method: We present Semantic Fractal Decomposition (SFD), a prompting technique that guides the model to recursively break down a given query or concept into smaller, self-similar components. At each level of decomposition, the model is asked to provide a confidence estimate. The process continues until a predefined depth is reached or the model indicates it can no longer meaningfully decompose the concept. The final uncertainty estimate is then constructed by aggregating these multi-level confidence scores using a novel fractal dimension-inspired algorithm. This approach allows for capturing uncertainty that may be present at different semantic scales and provides a more robust and consistent measure of the model’s confidence across varying levels of abstraction.
Experiment Plan: We will evaluate SFD on a diverse set of tasks ranging from simple factual queries to complex, multi-faceted questions in domains like philosophy, science, and law. We will compare its performance against traditional flat confidence estimation techniques and simpler hierarchical methods. Key metrics will include the consistency of uncertainty estimates across related queries at different levels of abstraction, the correlation between fractal-aggregated confidence scores and actual model performance, and the interpretability of the decomposition process.

Similarity: 0.81

## Appendix I Overlap Between AI Ranking and Expert Reranking

We show the overlap between the AI Ideas condition and the AI Ideas + Human Rerank conditions in Table 12. We note that 17 out of the 49 ideas in the AI Ideas + Human Rerank condition are also ranked as top ideas in the AI Ideas condition by the AI ranker, while the other 32 are not.

**Table 12: Overlap of ideas between AI + Human Rerank and AI conditions, broken down by topic.**
| Topic | Overlap | New |
| --- | --- | --- |
| Bias | 2 | 2 |
| Coding | 4 | 5 |
| Safety | 2 | 3 |
| Multilingual | 5 | 5 |
| Factuality | 2 | 9 |
| Math | 2 | 2 |
| Uncertainty | 1 | 5 |
| Total | 18 | 31 |

## Appendix J Quality Control of Human Expert Ideas

Each expert is instructed to choose one of the seven specified topics and write one idea on it within 10 days, following the given template in the annotation document.
We included an honor code statement to ask the participants to not use any AI tools in their idea writing.
We collected $N=50$ ideas originally and manually checked all of them for quality control. We filtered out one of them as being essentially a paraphrase of an existing paper’s abstract.
We compensated the participant nevertheless but excluded them from the review task.

## Appendix K Breakdown of Participant Positions

We show the detailed position breakdown of our 49 idea-writing participants in Table 13 and the positions of our 79 reviewer participants in Table 14.

**Table 13: Positions of the 49 idea writing participants.**
| Position | Count |
| --- | --- |
| Postdoc | 1 |
| PhD | 36 |
| Master | 9 |
| Undergraduate | 1 |
| Research Scientist | 1 |
| Machine Learning Engineer | 1 |

**Table 14: Positions of the 79 idea reviewing participants.**
| Position | Count |
| --- | --- |
| Postdoc | 7 |
| PhD | 63 |
| Master | 5 |
| Research Scientist | 3 |
| Machine Learning Engineer | 1 |

## Appendix L Institutions of the Idea Writing Participants

**Table 15: Institutions of the 49 idea writing participants.**
| Institution | Count |
| --- | --- |
| Stanford University | 11 |
| University of Southern California | 6 |
| University of Maryland | 3 |
| University of Illinois Urbana-Champaign | 3 |
| Johns Hopkins University | 3 |
| Columbia University | 2 |
| Carnegie Mellon University | 2 |
| University of Pennsylvania | 1 |
| Princeton University | 1 |
| Penn State University | 1 |
| Portland State University | 1 |
| Stony Brook University | 1 |
| University of Chicago | 1 |
| University of Washington | 1 |
| UC Berkeley | 1 |
| UCSD | 1 |
| Massachusetts Institute of Technology | 1 |
| George Washington University | 1 |
| Yale University | 1 |
| University of Toronto | 1 |
| Georgia Institute of Technology | 1 |
| National University of Singapore | 1 |
| Peking University | 1 |
| Tsinghua University | 1 |
| LinkedIn | 1 |
| Norm AI | 1 |

## Appendix M Institutions of the Idea Reviewing Participants

**Table 16: Institutions of the 79 reviewer participants.**
| Institution | Count |
| --- | --- |
| Stanford University | 25 |
| UC Berkeley | 4 |
| UT Austin | 4 |
| University of Maryland | 4 |
| Princeton University | 3 |
| University of Washington | 3 |
| University of Southern California | 3 |
| Carnegie Mellon University | 3 |
| University of Chicago | 2 |
| Johns Hopkins University | 2 |
| UCLA | 2 |
| Georgia Institute of Technology | 2 |
| University of Illinois Urbana-Champaign | 2 |
| Tsinghua University | 2 |
| Stony Brook University | 1 |
| Ohio State University | 1 |
| National University of Singapore | 1 |
| University of Michigan | 1 |
| Dartmouth College | 1 |
| Massachusetts Institute of Technology | 1 |
| University of Pennsylvania | 1 |
| University of Toronto | 1 |
| Portland State University | 1 |
| Penn State University | 1 |
| New York University | 1 |
| Columbia University | 1 |
| UC Santa Barbara | 1 |
| Brown University | 1 |
| Amazon | 1 |
| LinkedIn | 1 |
| Norm AI | 1 |
| AMD | 1 |

## Appendix N Mixed-Effects Models

One way to combine all the statistical tests above is to fit a linear mixed-effects model where we treat the condition as the fixed effect and other factors including reviewer and idea as random effects, while also accounting for the differences among different topics. This way, we can rely on the regression to account for all the possible confounders as the random effects.
Specifically, for each metric, we fit the following linear mixed-effects model:

This mixed-effects model analyzes the relationship between Score and Condition, while accounting for the hierarchical structure of the data. Fixed effects estimate the average effect of Condition on Score. Random intercepts for Topic allow for varying baseline scores across topics, and random slopes for Condition within each topic allow the effect of Condition to vary by topic. Additionally, variance components for ReviewerID and IdeaID account for variability in scores specific to individual reviewers and ideas, respectively.

The results are shown in Table 17.
The intercepts in the mixed-effects models represent the estimated mean score of the baseline condition, which in this context is the Human Ideas.
The coefficients for Condition[AI Ideas] and Condition[AI Ideas + Human Rerank] in the mixed-effects models represent the difference in the mean score for each metric between the AI ideas and the baseline (human ideas). For example, a positive coefficient of 0.761 for the novelty score means that AI Ideas, on average, score 0.761 points higher than Human Ideas on the novelty score metric; conversely, a negative coefficient of -0.330 for the feasibility score means that AI Ideas, score 0.330 points lower than Human Ideas on feasibility on average.
The topic (group) variance in the mixed-effects model represents the variability in the outcome metric that can be attributed to differences between the topics, which is relatively small in general.
Similarly, the idea variance and reviewer variance in the mixed-effects model represent the variability in the outcome metric that can be attributed to differences between individual ideas and between reviewers, respectively.
The reviewer variances are high in general, suggesting that there is substantial variability in how different reviewers rate the same ideas. This implies that reviewer differences play a significant role in the observed scores, with some reviewers consistently giving higher or lower ratings.

Overall, the results from the mixed-effects models confirm our main conclusion that AI ideas are rated as significantly more novel than human ideas.

**Table 17: Results of linear mixed-effects models. We bold results that are statistically significant (${}^{*}p<0.05;^{**}p<0.01;^{***}p<0.001$). Our main conclusion on AI ideas being more novel than human ideas still holds here.**
|  | Coef. | SE | $p$ |
| --- | --- | --- | --- |
| Novelty Score |  |  |  |
| Intercept | 4.826 | 0.217 | 0.000*** |
| Condition[AI Ideas] | 0.756 | 0.331 | 0.023* |
| Condition[AI Ideas + Human Rerank] | 0.902 | 0.305 | 0.003** |
| Idea Var | 0.412 | 0.178 |  |
| Reviewer Var | 0.803 | 0.202 |  |
| Excitement Score |  |  |  |
| Intercept | 4.493 | 0.212 | 0.000*** |
| Condition[AI Ideas] | 0.626 | 0.303 | 0.039* |
| Condition[AI Ideas + Human Rerank] | 0.879 | 0.298 | 0.003** |
| Idea Var | 0.495 | 0.227 |  |
| Reviewer Var | 0.782 | 0.167 |  |
| Feasibility Score |  |  |  |
| Intercept | 6.595 | 0.224 | 0.000*** |
| Condition[AI Ideas] | -0.300 | 0.294 | 0.307 |
| Condition[AI Ideas + Human Rerank] | -0.183 | 0.314 | 0.561 |
| Idea Var | 0.476 | 0.188 |  |
| Reviewer Var | 1.035 | 0.261 |  |
| Expected Effectiveness Score |  |  |  |
| Intercept | 5.156 | 0.211 | 0.000*** |
| Condition[AI Ideas] | 0.310 | 0.140 | 0.027* |
| Condition[AI Ideas + Human Rerank] | 0.383 | 0.242 | 0.114 |
| Idea Var | 0.200 | 0.151 |  |
| Reviewer Var | 0.469 | 0.141 |  |
| Overall Score |  |  |  |
| Intercept | 4.660 | 0.242 | 0.000*** |
| Condition[AI Ideas] | 0.137 | 0.294 | 0.640 |
| Condition[AI Ideas + Human Rerank] | 0.610 | 0.320 | 0.056 |
| Idea Var | 0.262 | 0.154 |  |
| Reviewer Var | 1.071 | 0.225 |  |

## Appendix O Score Breakdown by Topic

We show the breakdown of all scores across all conditions by topic. Note that due to the smaller sample sizes for the per-topic breakdown, most results are not statistically significant and only offer an intuitive understanding of the trends.

Figure: Figure 5: Breakdown of all scores by topic.
Refer to caption: x5.png

## Appendix P Example Idea: Modular Calibration for Long-form Answers

## Appendix Q Example Idea: Semantic Resonance Uncertainty Quantification

## Appendix R Example Idea: Translation with LLMs through Prompting with Long-Form Context

## Appendix S Example Idea: Linguistic Pivot Constellation: Enhancing Cross-Lingual Transfer for Low-Resource Languages and Dialects

## Appendix T Example Idea: LLM Directed Retrieval Querying for Improving Factuality

## Appendix U Example Idea: Semantic Divergence Minimization: Reducing Hallucinations in Large Language Models through Iterative Concept Grounding

## Appendix V Example Idea: Autoprompting: Generate Diverse Few-shot Examples for Any Application

## Appendix W Example Idea: Temporal Dependency Unfolding: Improving Code Generation for Complex Stateful Systems

## Appendix X Identities of Example Ideas

We reveal whether each example idea is AI-generated or human-written:

- •
Human ideas:
Example P, Example R, Example T, Example V
- •
AI ideas:
Example Q, Example S, Example U, Example W

## Appendix Y Attempt on Idea Execution Agent

For our execution agent, the input is the generate idea (the full project proposal), and the output is a Python file that can be executed with our specified command.
Since there is often a common pipeline of implementing prompting-based research ideas, we provide a manually crafted code file example as template. We attach the full template below:

As seen above, we have defined two different evaluator functions. The style_evaluator() checks whether all components mentioned in the proposed method are implemented, and the output_evaluator() checks whether the model predictions are correct.

We provided this template along with instructions to tell the execution agent to modify all necessary functions for the provided idea. We tested the agent on 30 safety prompting ideas and 30 factuality prompting ideas.

**Table 18: Success rate of our execution agent on 30 randomly sampled AI ideas on safety and factuality.**
|  | Safety | Factuality |
| --- | --- | --- |
| Total | 30 | 30 |
| Executed | 17 | 18 |
| Passed Style Evaluator | 13 | 14 |
| Proposed Method Works Better | 5 | 1 |

As shown in Table 18, over half of the generated code files can be successfully executed, and the results indicate that only a handful of these proposed methods actually outperform the baselines.
While this shows some promise that the execution agents can automatically implement and identify effective methods out of all the generated ideas, a careful manual check reveals many pitfalls.

For example, the agent implemented the following baseline for the task of identifying malicious and adversarial prompts, which is a keyword-based approach that is trivial to beat with any LLM-based methods.

In another example, for the same task of detecting adversarial prompts, the agent implemented the following evaluator function:

The agent is supposed to inject adversarial triggers into sentiment classification data to test whether the proposed method can detect those adversarial prompts while maintaining sentiment classification accuracy. However, the agent only evaluates the accuracy on the original sentiment classification task but not the task of adversarial prompt detection.

Given these errors, we believe more work is needed to carefully verify the code implementations produced by the execution agent rather than blindly trusting their executed results, and we leave such attempts to future work.

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 6
presentation: 3
questions: 1) RAG pipeline ablation tests: "We keep the top k = 20 papers from each executed function call and stop the action generation when a max of N = 120 papers have been retrieved." , "For retrieval augmentation, we randomly select k = 10 papers from the top-ranked retrieved papers" . Can you please provide some small scale (self-evaluated) tests ablating these parameters to ensure their optimality?

2) Researcher Background: I understand that the researcher familiarity numbers are provided for idea generation and reviewing processes, however it is slightly different from expertise. So, a better way to ensure the robustness of results is to look at the distribution of the expertise of the reviewers. 

3) Interestingness: Can you please clarify the meaning of the term? Is it that the insight behind the proposed idea a non-trivial one or is it that the human evaluating it finds it personally interesting?
soundness: 3
strengths: 1) Extensive human evaluation with over 100 NLP researchers
2) Great care taken to remove confounders through style normalization, review form, grant submission form for research ideas etc. 
3) Interesting insights like lack of ability of LLMs to generate diverse research ideas.
summary: This paper evaluates if LLM based RAG agents are capable of generating novel research ideas for prompting techniques by comparing them with expert researchers. The key contributions of their work are 

1) Evaluating the capabilities of LLMs to generating research ideas and analyzing their strengths (novelty) and weaknesses (feasibility and diversity)
2) They focus on removing confounders by conducting a large scale human evaluations and controlling experimental setting
weaknesses: 1) Not strong enough motivation for restricting to prompting based research. This could have been a confounder since we did we are not sure about the ability of LLMs to generate research where the solutions are more complex. 

2) Usefulness: The paper does not talk about the usefulness of the generated ideas research ideas, which is more important than novelty. Research ideas are born out of deep insights into the limitations of previous research. This paper neither gives the discussion/limitations section of research papers to the LM as context nor does it do any analysis of the usefulness of the generated ideas. Using abstracts and paper titles sounds like an anti-causal way of generating ideas which I think could affect the usefulness.

---

confidence: 4
contribution: 2
initial_rating: 6
presentation: 4
questions: - The authors used reviews from Openreview, are there any licensing issues?
soundness: 4
strengths: * Thorough Study Design: This study is detailed and carefully planned, taking a year to complete and requiring significant resources. It includes human expert baselines and a clear evaluation process, with strong statistical methods that make the results reliable.

* Key Conclusion: The study makes an important finding about the differences between AI-generated and human-generated research ideas, adding to our understanding of AI's potential in coming up with research ideas. — more discussions on the absolute quality, in addition to the comparison between AI and human, would be more apprieciated (e.g. are the quality of both ideas bad).

* Interesting Findings: The study also reveals some surprising insights, such as AI-generated ideas becoming repetitive as more ideas are created, and that current AI systems aren’t yet good at reliably evaluating research ideas
summary: - Study: This paper presents a novel study comparing research ideas generated by Large Language Models (LLMs) with those crafted by expert NLP researchers.
- Methodology: The study involves hired expert researchers who generate ideas and conduct blind reviews of both human- and AI-generated ideas.
- Conclusion: This work is the first to conclude that AI-generated ideas are judged as significantly more novel than those from human experts (p < 0.05).
weaknesses: * While this work is interesting and presents important conclusions, it does not center on machine learning methodology or technical analysis. Although it is relevant and intriguing for the ML community, the paper leans more towards evaluating scientific idea generation, aligning more with applications rather than with core ML research.
* The study is limited by its exclusive focus on prompting-based NLP research, which restricts the generalizability of its findings to other fields or other ML directions.
* Although the authors worked to standardize evaluation criteria, research ideation remains inherently subjective, as reflected in the study's inter-reviewer agreement of 56.1%

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. Could you provide the specific prompt used for the Agent to generate ideas?
2. Observing that some researchers in Table 3 and Table 4 have a familiarity level of 1 with the domain (which could be considered as low-quality annotation), what are the experimental results in Table 5 after removing this data?
soundness: 3
strengths: 1. The results are quality assured due to significant investment. The author recruited a total of 138 excellent natural language processing researchers and verified their quality through Google Scholar. On average, each idea took the researchers 5.5 hours to generate, and there was a reward of $300. As for the evaluators, each evaluation opinion had a reward of $25. This incentive mechanism, to a certain extent, ensures the quality of idea generation and evaluation.
2. It is relatively fair. By standardizing the topics and providing human experts and large language models with the same writing templates, and finally passing both outputs through the same style transformation module for rewriting, the consistency of style between the two is ensured, minimizing human bias towards writing style as much as possible.
3. It is very meaningful. This large-scale manual evaluation verifies the ability of large language models to generate scientific ideas comparable to those of human experts, providing motivation for the development of subsequent work.
summary: They constructed a meticulously controlled experiment comparing human-generated and LLM-generated ideas, thus overcoming the sample size and baseline issues present in previous small-scale evaluation studies. The study recruited 138 outstanding natural language processing researchers to generate human baseline ideas and conduct blind reviews (49 were responsible for writing ideas, and 79 for blind review). To reduce the impact of confounding factors, the study strictly controlled the style of the ideas and standardized the topic distribution between human and LLM-generated ideas. After a year-long high-cost evaluation, the authors provided a human expert baseline and a standardized evaluation protocol, laying a foundation for future related research.

In nearly 300 reviews, the authors found that under multiple hypothesis tests and different statistical tests, AI-generated ideas were considered more novel than those of human experts, but less feasible. In addition to evaluating the ideas, the study also analyzed the LLM agent, revealing its limitations and unresolved issues. Despite high hopes for expanding LLMs' reasoning abilities, the authors found that they still show deficiencies in the diversity of idea generation, and the results of human consistency analysis indicate that LLMs cannot yet serve as reliable evaluators of scientific idea quality.

In summary, the contributions of the paper are as follows:

1. Standardized some evaluation criteria for scientific ideas generated using large language models while demonstrating the limitations of LLMs as judges in evaluating scientific ideas.
2. Conducted a blind review evaluation of the quality of scientific ideas generated by LLMs through the recruitment of a large number of scientists, verifying both the potential and shortcomings of LLMs in the field of scientific research.
weaknesses: 1. The author concluded that ideas generated by large language models lack diversity. However, using the same prompts, especially longer prompts, it is easy for large models to produce similar responses. Therefore, the lack of diversity may not be solely due to the models themselves; it could also be due to a lack of diversity in the prompts. The author needs to test more prompts to sufficiently reach such a conclusion for this experiment.
2. The paper only verifies the generation quality of the author's simple implementation of an agent, lacking evaluation against other baselines.
3. The research is limited to the NLP field and lacks studies in other domains.

---

confidence: 3
contribution: 3
initial_rating: 5
presentation: 3
questions: See above.
soundness: 2
strengths: 1. The topic is interesting, as employing LLMs to generate research ideas holds practical value for researchers. Additionally, the findings offer valuable insights that can help researchers determine whether to adopt LLMs as part of their research toolkit.

2. The study’s experimental design is well-structured, effectively controlling for potential confounding factors. Through the use of blind reviews and standardized styles and topics, the authors achieve a fair and balanced comparison between ideas generated by humans and those by LLMs.

3. This paper incorporates a large-scale human evaluation, involving over 100 expert NLP researchers to create and assess research ideas. This approach provides a robust evaluation of the ideas across multiple metrics.
summary: This paper investigates the potential of LLMs to autonomously generate innovative research ideas comparable to those created by human experts. 
In a controlled study, over 100 NLP researchers evaluated ideas generated by both humans and an LLM agent across metrics like novelty, excitement, feasibility, and effectiveness. The findings reveal that LLM-generated ideas were rated significantly more novel than human ideas, though slightly lower on feasibility.

The authors addressed multiple experimental challenges, including ensuring a balanced comparison between human and LLM ideas by standardizing topics and using blind reviews. 
They also tested the robustness of results through various statistical methods. The LLM utilized a retrieval-augmented generation approach and scaled inference techniques to produce numerous ideas, from which a ranking mechanism selected the best ones. Despite the promising novelty scores, the study identifies several limitations of LLMs in research ideation, particularly a lack of diversity in ideas and challenges with reliable self-evaluation.

The study highlights open questions in designing research agents and emphasizes the potential and current limitations of LLMs in academic innovation.
weaknesses: 1. As noted in L434-L435, I appreciate that the authors acknowledge the subjectivity involved in reviewing ideas rather than executed papers. However, it would still be valuable to compare LLM-generated ideas with those from published papers (as ground truth), as the latter have been deemed at least ``feasible''.

2. According to L175-L179, the LLM agent was created with a minimalist approach, incorporating only paper retrieval, idea generation, and ranking. However, in real-world research, high-quality ideas often benefit from iterative refinement and prototyping, which are essential for improving clarity and feasibility. As such, I view this paper as an initial investigation into whether LLMs, in their current, straightforward form, can generate high-quality research ideas. The limitations observed, such as the lower feasibility of LLM-generated ideas, could potentially be addressed by a more sophisticated agent framework that includes stages for refining and prototyping ideas.

3. I would not consider this as a major weakness, but given the insights provided, it would be beneficial if the authors offered best practices or recommendations for using LLMs effectively to generate research ideas.

Meta-review: **Summary**

This paper aims to study whether LLMs can be scientifically creative and then evaluates if LLM based RAG agents are capable of generating novel research ideas for prompting techniques by comparing them with expert researchers. 
This paper addresses the question that we all don't want to be answerd as discussed in [1]. 


**Strengths**

The extensive analysis made using NLP researchers

**Weaknesses**
Clearly, this kind of paper has many weaknesses as we don't want LLMs to succed in this task.
- The usefulness of the generated ideas does not seems to be proven (reviewer *3XGM* is not convinced)


**Final remarks**

The paper touches important issues.

** References **

[1] GASP! Generating Abstracts of Scientific Papers from Abstracts of Cited Papers

Decision: Accept (Poster)