PaperlayerView exact revision on arXiv

Source-linked AI summary

SciPIP: An LLM-based Scientific Paper Idea Proposer

Wenxiao Wang, Lihui Gu, Liye Zhang, Yunxiang Luo, Yi Dai, Chen Shen, Liang Xie, Binbin Lin, Xiaofei He, Jieping Ye

arXiv:2410.23166v2cs.CLcs.AIcs.IRcs.LG

TL;DR

Existing scientific idea-generation approaches leave gaps in retrieval and use of retrieved literature. SciPIP combines structured multi-signal retrieval with generation from both retrieved papers and LLM knowledge, and outperforms existing approaches on idea-quality dimensions in NLP and CV. Its evaluation is limited to those two fields.

  • Problem

    Existing idea-generation work faces limitations in its literature retrieval and in how it uses retrieved papers.

    Source: §1 · p. 1

  • Method

    SciPIP builds a database supporting keyword, semantic, and citation-based retrieval, then integrates ideas generated from retrieved literature with ideas from LLM internal knowledge.

    Source: §1 · p. 2

  • Results

    SciPIP significantly outperforms existing idea-generation approaches in NLP and CV on novelty, feasibility, clarity, and relevance.

    Source: §1 · p. 2

  • Takeaways & Limitations

    SciPIP generates ideas with strong potential on novelty, feasibility, and clarity in the evaluated domains.

    Source: §5 · p. 8

    The study focuses on NLP and CV, and feasibility was evaluated subjectively by humans.

    Source: §5 · pp. 8–9

Abstract

from arXiv · show

The rapid advancement of large language models (LLMs) has opened new possibilities for automating the proposal of innovative scientific ideas. This process involves two key phases: literature retrieval and idea generation. However, existing approaches often fall short due to their reliance on keyword-based search tools during the retrieval phase, which neglects crucial semantic information and frequently results in incomplete retrieval outcomes. Similarly, in the idea generation phase, current methodologies tend to depend solely on the internal knowledge of LLMs or metadata from retrieved papers, thereby overlooking significant valuable insights contained within the full texts. To address these limitations, we introduce SciPIP, an innovative framework designed to enhance the LLM-based proposal of scientific ideas through improvements in both literature retrieval and idea generation. Our approach begins with the construction of a comprehensive literature database that supports advanced retrieval based not only on keywords but also on semantics and citation relationships. This is complemented by the introduction of a multi-granularity retrieval algorithm aimed at ensuring more thorough and exhaustive retrieval results. For the idea generation phase, we propose a dual-path framework that effectively integrates both the content of retrieved papers and the extensive internal knowledge of LLMs. This integration significantly boosts the novelty, feasibility, and practical value of proposed ideas. Our experiments, conducted across various domains such as natural language processing and computer vision, demonstrate SciPIP's capability to generate a multitude of innovative and useful ideas. These findings underscore SciPIP's potential as a valuable tool for researchers seeking to advance their fields with groundbreaking concepts.

1. Introduction

SciPIP addresses challenges in literature retrieval and LLM-based idea generation with a literature database, multi-granularity retrieval, and a dual-path generation framework. Experiments in NLP and CV report better idea quality than existing approaches.

1. Introduction

  • Existing retrieval methods often rely on keywords or broad section embeddings, while idea-generation methods may underuse retrieved papers.The paper argues these approaches can limit retrieval thoroughness or miss useful details from literature.Source: §1 · p. 1
  • SciPIP builds a literature database of about 78K AI-conference papers, summarizing each into keywords, background, ideas, concise methods, and references.The extracted components are preprocessed and stored to support retrieval.Source: §1 · pp. 1–2
  • Its multi-granularity retrieval combines keywords, semantic embeddings, and citation relations to retrieve literature across multiple dimensions.The approach is designed to make retrieval more thorough.Source: §1 · p. 2
  • The dual-path framework draws on both LLM internal knowledge and retrieved literature, then integrates the two pathways into final ideas.The paper presents this as a way to combine insights from both sources.Source: §1 · p. 2
  • SciPIP significantly outperforms existing idea-generation approaches in NLP and CV on novelty, feasibility, clarity, and relevance.The experiments evaluate proposed ideas with human experts.Source: §1 · p. 2
  • The authors also construct an AI-paper database with extracted key information and embeddings for community use.Source: §1 · p. 2

2. Related Works

Prior LLM-based scientific idea systems commonly combine literature retrieval and idea generation, but vary in retrieval sources and in how much paper content they use. The paper situates SciPIP alongside retrieval-augmented generation and text-embedding approaches.

LLM-based Scientific Idea Proposal

  • Prior idea-generation systems use keyword search, metadata vectors, or citation lists for retrieval, and some primarily draw on abstracts for idea generation.AI Scientist uses keyword search and internal model knowledge; ResearchAgent identifies problems in retrieved papers and proposes solutions (Baek et al., 2024).Source: §2 · p. 2

Retrieval Augmented Generation

  • Scientific idea proposal resembles retrieval-augmented generation, but broad domain-relevant retrieval can return excessive, redundant literature.Typical RAG systems combine indexing, retrieval, and generation (Gao et al., 2023b).Source: §2 · p. 2

Text Embedding Models

  • Text embeddings encode semantic meaning as vectors; retrieval embeddings are often asymmetric, while text-matching embeddings are symmetric.In retrieval, query and retrieved text may use different models or prompts; in matching, both segments are encoded in the same manner.Source: §2 · p. 2

3. Methods

SciPIP combines a structured literature database and interconnected retrieval signals with two idea-generation paths. Retrieved papers and LLM internal knowledge contribute separately before ideas are integrated and expanded.

3. Methods

  • SciPIP’s methods comprise database construction, multi-granularity retrieval, and dual-path idea generation using retrieved literature and LLM knowledge.Source: §3 · p. 3

3.1. Literature Database Construction

  • The database summarizes papers’ title, abstract, and introduction for background and ideas, and also extracts keywords and concise methods for indexing.Extracted background and ideas are encoded as embeddings; the database also stores a paper-keyword graph based on co-occurrences.Source: §3.1 · pp. 3–4

3.2. Multi-granularity Literature Retrieval

  • SKC retrieval interlinks semantic, keyword, and citation signals, then clusters papers with similar ideas to reduce redundancy.Semantic retrieval matches query backgrounds to paper backgrounds; keyword expansion and co-citation add further candidates, while clustering selects papers across groups.Source: §3.2 · pp. 4–5

3.3. Dual-path Idea Generation

  • The dual-path framework generates ideas from retrieved literature and from LLM internal knowledge, then integrates and refines both sets.Source: §3.3 · p. 5

3.3.1. BRAINSTORM-BASED GENERATION

  • For brainstorming, SciPIP first expands a user query into a structured background, generates three to four ideas, and uses their keywords to broaden subsequent retrieval.Source: §3.3.1 · p. 5

3.3.2. RETRIEVAL BASED GENERATION

  • For literature-based generation, SciPIP summarizes retrieved methods, extracts and condenses relevant inspirations, and uses them with the transformed background to generate ideas.Few-shot examples guide methodology summaries; a concise-method example guides integration and expansion into more detailed ideas.Source: §3.3.2 · p. 6

4. Experiments

Experiments evaluate SciPIP’s ideas with human ratings across NLP and CV and test its retrieval choices through ablations. The reported results favor SciPIP over comparison systems and identify SKC as the most balanced retrieval method.

4.1. Implementation Details

  • The database contains 78,571 papers from nine major ML, NLP, and CV conferences; GPT-4o summaries are encoded with jina-embedding-v3 for retrieval.Source: §4.1 · p. 6
  • Tests use 34 ACL 2024 and 25 CVPR 2024 papers not prereleased on arXiv; 12 experienced researchers assess ideas on five dimensions.The evaluators are graduate students or PhD candidates, each with at least one first-author top-tier conference paper.Source: §4.1 · p. 6

4.2. Results and Analysis

  • All three SciPIP versions outperform GPT-4o and AI Scientist on favorable idea ratings; the comparison uses three backgrounds and about 30 ideas per framework.Favorable ratings are those rated ≥4.Source: §4.2 · p. 7, Figure 4 · p. 7
  • The passage reports sufficient feasibility (24.14% vs. 35.33%); SciPIP integrates brainstorming and retrieval-based generation, achieving an improved balance between novelty and feasibility while yielding improved results.Source: §4.2 · p. 7
  • Human researchers rated 80% of SciPIP ideas as more novel than AI Scientist’s, and SciPIP performed better across evaluation metrics.The comparison uses matched backgrounds inferred from AI Scientist ideas.Source: §4.2 · p. 7
  • SKC achieves the most balanced and superior performance across evaluated metrics; retrieved-paper count shows no significant rating correlation, so later experiments use 10 papers.SKC is the proposed multi-granularity method; Figure 6 compares retrieval methods and retrieved-paper counts.Source: §4.1 · p. 6, Figure 6 · p. 8, §4.2 · p. 8
  • For NLP ideas, most novelty, clarity, and feasibility ratings are ≥3, over 66% of relevance ratings are ≥4, and 53.14% are rated helpful.Source: §4.2 · p. 8
  • A demonstrated SciPIP idea, rated 4 for novelty and feasibility, proposes representation-based importance and semantic twin networks to extract key information from long documents.The example addresses simplistic chunking in retrieval-augmented generation systems.Source: §4.2 · p. 8, Table 3 · p. 8

5. Conclusions and Limitations

SciPIP is presented as an effective method for generating scientific paper ideas in NLP and computer vision. The study notes that feasibility judgments are subjective and its evaluation covers only those two fields.

5. Conclusions and Limitations

  • SciPIP generates ideas with potential in novelty, feasibility, clarity, and other critical dimensions.The study demonstrates its effectiveness in NLP and computer vision.Source: §5 · p. 8
  • Feasibility of the generated ideas was evaluated subjectively by humans.Source: §5 · p. 8
  • The study evaluates SciPIP in two fields: NLP and computer vision.The authors intend to extend it to other fields to assess broader applicability and generalizability.Source: §5 · pp. 8–9

A. Related Works About Literature Retrieval Tools

Literature retrieval tools differ in their access to full text and disciplinary scope, while keyword matching can leave users to manually filter imprecise results.

A. Related Works About Literature Retrieval Tools

B. SciPIP Proposed Ideas.

The appendix presents a SciPIP-generated idea demonstration in Table 3 and points to supplementary materials for other examples.

B. SciPIP Proposed Ideas.

  • Appendix B contains a SciPIP-generated idea demonstration in Table 3, with other proposed-idea demonstrations in the supplementary materials.Source: B. SciPIP Proposed Ideas · p. 12

B.1. Detailed Idea

The proposed document-thinning approach combines attention-based selection with context-and-focus semantic projections to produce a reduced, coherent document synthesis.

B.1. Detailed Idea

  • Multi-Stage Contextual Thinning with Semantic Twin Projections reduces document content in two phases while aiming to preserve semantic integrity and contextual relevance.Source: §B.1 · p. 12

B.1.1. OVERVIEW OF THE MULTI-STAGE CONTEXTUAL THINNING PROCESS

  • The process consists of Dynamic Attention-Based Document Thinning followed by Semantic Twin Projections, progressively refining relevance and coherence before generation.Source: §B.1.1 · p. 12

B.1.2. PHASE 1: DYNAMIC ATTENTION-BASED DOCUMENT THINNING

  • The first phase applies attention mechanisms to focus on contextually important parts of a document.Source: §B.1.2 · p. 12

Dual Embedding Representation

  • Dual embeddings represent overarching themes with Eglobal and granular details with Elocal, from which global and local attention scores are computed.The query and key matrices are derived from the respective embeddings.Source: Dual Embedding Representation · p. 12

Broad Stroke Thinning

  • Acombined weights global and local attention scores using tunable α, and the highest-scoring document segments form the reduced document.The selection filters less relevant material while retaining core content.Source: Broad Stroke Thinning · p. 12

B.1.3. PHASE 2: SEMANTIC TWIN PROJECTIONS

  • The second phase uses semantic twin networks to further emphasize key information in the thinned document.Source: §B.1.3 · p. 12

Semantic Twin Network Configuration

Targeted Chunk Identification

  • Segments are prioritized by cosine similarity between context and focus projections, and high-priority segments form the final synthesis.The similarity measures alignment between the two network outputs.Source: Targeted Chunk Identification · p. 13

B.1.4. INTEGRATION WITH RAG SYSTEMS

Refined document segments feed the generative module; minimizing document scope reduces computational overhead and enhances semantic coherence in generated outputs.

B.1.4. INTEGRATION WITH RAG SYSTEMS

  • Minimizing document scope reduces computational overhead and enhances semantic coherence in generated outputs.The refined content is then supplied to the generative module.Source: §B.1.4 · p. 13

B.1.5. BENEFITS

The benefits described include semantically guided segmentation, improved coherence, and more efficient processing that maintains response quality.

B.1.5. BENEFITS

  • Semantic relevance, rather than arbitrary heuristics, guides document segmentation.The approach uses dynamic attention-based and semantic projections.Source: §B.1.5 · p. 13
  • Preserving logical connections between segments enhances generative-response quality.Source: §B.1.5 · p. 13
  • Narrowing documents to their most relevant components reduces generative resource use while maintaining response quality.The approach also addresses traditional RAG limitations with lengthy documents and supports more refined, efficient processing.Source: §B.1.5 · p. 13

C. Keyword Filtering

Keyword filtering addresses noisy neighborhood-based entity expansion, while the prompt templates specify how background information is used to generate and develop ideas.

C. Keyword Filtering

  • Keyword neighborhoods can add irrelevant co-occurring entities, especially when a common term such as “Transformer” appears across many papers.Source: C. Keyword Filtering · p. 13
  • The filtering mechanisms require co-occurrence in at least m papers and retain the n lowest-frequency entities across the literature database.The frequency rule reflects the authors’ hypothesis that database-wide frequent entities are less representative.Source: C. Keyword Filtering · p. 13

D. Prompt Templates

Related papers

Loading 2410.23166v2…