Papers by Dhairya Dalal
Inference to the Best Explanation in Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have found success in real-world applications, but their underlying explanatory process is still poorly understood. |
| Approach: | They propose to use a framework inspired by philosophical accounts on Inference to the Best Explanation (IBE) to advance the interpretation and evaluation of LLMs’ explanations. |
| Outcome: | The proposed framework can identify the best explanation with up to 77% accuracy (27% above random) while being intrinsically more efficient and interpretable. |
CALM-Bench: A Multi-task Benchmark for Evaluating Causality-Aware Language Models (2023.findings-eacl)
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| Challenge: | Recent advances in foundation language models have shown the efficacy of pre-trained models across diverse QA tasks. |
| Approach: | They propose a multi-task benchmark for evaluating causality-aware language models to unify causal QA research. |
| Outcome: | The proposed model outperforms single-task fine-tuned models on the CALM-Bench tasks. |
PEIRCE: Unifying Material and Formal Reasoning via LLM-Driven Neuro-Symbolic Refinement (2025.acl-demo)
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| Challenge: | Large Language Models (LLMs) are capable of material inference but lack formal rigour and verifiability. |
| Approach: | They propose a framework to unify material and formal inference through an iterative conjecture–criticism process. |
| Outcome: | The proposed framework unifies material and formal inference through an iterative conjecture–criticism process. |