HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation (2504.11524v1)

Abstract: There is growing interest in hypothesis generation with LLMs. However, fundamental questions remain: what makes a good hypothesis, and how can we systematically evaluate methods for hypothesis generation? To address this, we introduce HypoBench, a novel benchmark designed to evaluate LLMs and hypothesis generation methods across multiple aspects, including practical utility, generalizability, and hypothesis discovery rate. HypoBench includes 7 real-world tasks and 5 synthetic tasks with 194 distinct datasets. We evaluate four state-of-the-art LLMs combined with six existing hypothesis-generation methods. Overall, our results suggest that existing methods are capable of discovering valid and novel patterns in the data. However, the results from synthetic datasets indicate that there is still significant room for improvement, as current hypothesis generation methods do not fully uncover all relevant or meaningful patterns. Specifically, in synthetic settings, as task difficulty increases, performance significantly drops, with best models and methods only recovering 38.8% of the ground-truth hypotheses. These findings highlight challenges in hypothesis generation and demonstrate that HypoBench serves as a valuable resource for improving AI systems designed to assist scientific discovery.

• The paper introduces HypoBench, a benchmark designed to standardize evaluation of hypothesis generation methods using large language models.
• The paper details a comprehensive framework that employs diagnostic metrics like HDR, FDR, and RC alongside controlled synthetic and real-world datasets.
• The paper demonstrates that data-driven methods, particularly those combining literature with empirical data, outperform baseline approaches on varied tasks.

This paper introduces HypoBench, a benchmark designed for the systematic evaluation of hypothesis generation methods, particularly those using LLMs. It addresses the lack of standardized evaluation practices in this emerging field by defining the task, proposing relevant evaluation metrics, and providing a diverse set of datasets.

Problem Definition:

Hypothesis generation is defined as producing natural language theories or explanations ($H$) for observed phenomena ($Q$), given observational data ($D = \{(x, y)\}$) and potentially relevant literature ($L_Q$). This contrasts with research ideation, which focuses more on generating novel research directions rather than explaining observed data patterns. The core challenge involves inductive reasoning (identifying latent patterns $z = g(x)$) and abstraction/communication (verbalizing these patterns $H$ from $z$ such that $y=f(z)$).

HypoBench Components:

1. Datasets (194 total across 12 domains):
   • Real-world (7 domains): Tasks like deception detection, AI content detection, persuasive argument prediction, mental stress detection, news headline engagement, retweets, and a new paper citation prediction task. These datasets include in-domain (IND) and out-of-domain (OOD) splits to test generalizability. Ground truth hypotheses are unknown.
   • Synthetic (5 domains): Tasks like presidential election prediction, personality prediction, marine ecosystem analysis, college admission, and shoe sales. These are crucial for controlled evaluation as the ground-truth data generation process ($y = f(z)$) and the mapping from latent features to text ($z = g(x)$) are known.
2. Synthetic Data Generation (Implementation Detail):
   • Underlying Model ($f$): Uses either Logistic Regression or a Decision Tree to define the relationship between latent features ($z$) and the outcome ($y$).
   • Text Generation ($g^{-1}$): Uses LLM prompts to generate textual observations ($x$) based on the latent features ($z$).
   • Difficulty Controls (Practical Aspect): Allows systematic testing by varying:
     • Noise in outcomes (label flipping/probabilistic sampling).
     • Number of relevant features ($Z$).
     • Compositionality (Decision Tree depth, requiring feature interactions).
     • Number of distractor features ($Z^0$).
     • Textual subtlety (explicit vs. implicit/nuanced feature mentions in text).
3. Evaluation Framework: Focuses on explanatory power first, then other qualities.
   • Hypothesis Discovery Rate (HDR) (Synthetic Only): Measures how well generated hypotheses $\hat{H}$ match ground truth $H$.
     • $HDR = FDR \times RC$
     • Feature Discovery Rate (FDR): $FDR = \frac{|\hat{Z} \cap Z|}{|Z|}$. Measures the proportion of true latent features ($Z$) correctly identified ($\hat{Z}$).
     • Relationship Correctness (RC): $$RC = \frac{1}{|\hat{Z} \cap Z|} \sum_{z_i \in \hat{Z} \cap Z} M_r(z_i, \hat{f}, f)$$. Measures if the relationship between identified features and the outcome ($\hat{f}$) matches the ground truth ($f$), evaluated using an LLM judge ($M_r$, e.g., GPT-4o).
   • Practical Utility: Classification accuracy using the generated hypothesis ($\hat{f}, \hat{Z}$) to predict labels for test data ($X$) via an LLM inference model ($M_I$).
   • Generalizability (Real-world Only): Accuracy/F1 difference between IND and OOD splits, and cross-model evaluation (hypotheses generated by one model, used for inference by another).
   • Qualitative Metrics: Novelty, Plausibility, Clarity, rated on a 1-5 scale by an LLM judge ($M_q$, e.g., GPT-4o) using literature ($L_Q$) as context.

Experiments:

• Models: GPT-4o-mini, Qwen-2.5-72B-Instruct, Llama-3.1-70B-Instruct, DeepSeek-R1-Distilled-Llama-70B. A finetuned Llama-3.1-8B is used as a strong baseline on real data.
• Methods:
  • Baselines: Zero-shot/Few-shot inference (classification without explicit hypothesis generation).
  • Zero-shot hypothesis generation (using only LLM's internal knowledge).
  • Literature-only generation [liuliteraturemeetsdata].
  • IO Prompting qiu2024phenomenal.
  • Iterative Refinement qiu2024phenomenal.
  • HypoGeniC zhou2024hypogenic.
  • Literature + Data liuliteraturemeetsdata.

Key Findings & Practical Implications:

1. Method Performance: Data-driven methods (IO Prompting, Iterative Refinement, HypoGeniC, Literature + Data) significantly outperform simple inference or generation based only on literature/pre-trained knowledge. Literature + Data generally performs best on real-world tasks, highlighting the value of combining empirical data with existing knowledge. HypoGeniC often performs best on synthetic tasks.
2. Model Performance:
   • On real-world tasks (OOD), Qwen achieved the highest practical utility (accuracy/F1) when paired with the best method (Literature + Data), suggesting strong hypothesis generation capabilities. However, it benefited less from adding literature compared to other models. Llama-3.1 performed best in few-shot inference.
   • On synthetic tasks, DeepSeek performed best on base difficulty (HDR ~94%) but was highly sensitive to noise and distractors. GPT-4o-mini was less sensitive but started lower. Qwen and Llama handled simple (depth 2) feature interactions well.
3. Difficulty Impact: Performance (HDR) degrades substantially as synthetic task difficulty increases (noise, distractors, compositionality depth > 2, textual subtlety). The best methods/models recovered only 38.8% of ground-truth hypotheses in complex synthetic scenarios. This indicates significant room for improvement in robustness and handling complex patterns.
4. Model Priors: Models exhibit different inherent biases. Zero-shot generation performance varied significantly across synthetic tasks (e.g., better at 'College Admission' than 'Presidential Election'), suggesting priors align better with some domains. Performance dropped significantly on counterintuitive versions of tasks, especially at high complexity (average HDR < 15%), indicating models struggle when priors are misleading.
5. Qualitative Trade-offs: Methods struggle to balance plausibility and novelty. Literature-only methods yield plausible but less novel hypotheses, while methods like Iterative Refinement generate more novel but potentially less plausible ones.
6. Generalizability: Hypotheses showed reasonable generalization across Qwen, Llama, and DeepSeek, but less so with GPT. Performance on OOD real-world data for the best methods was comparable to a finetuned model, but lagged significantly on IND data, suggesting potential for discovering more specific patterns.

Conclusion for Practitioners:

HypoBench provides a valuable resource for evaluating and developing AI systems for hypothesis generation. It offers diverse datasets (real and synthetic with controlled difficulty) and a multi-faceted evaluation framework (HDR, utility, generalization, qualitative aspects). The results show current SOTA methods can discover meaningful patterns but are brittle to noise, complexity, and misleading priors. The benchmark highlights the need for more robust methods capable of handling complex interactions and noisy data. The synthetic datasets, in particular, allow for precise measurement of discovery rates and targeted improvements. The code and datasets are publicly available at \url{https://chicagohai.github.io/HypoBench/}.