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Neural models, including large language models (LLMs), achieve superior performance on logical reasoning tasks such as question answering. To elicit reasoning capabilities from LLMs, recent works propose using the chain-of-thought (CoT) mechanism to generate both the reasoning chain and the answer, which enhances the model’s capabilities in conducting reasoning. However, due to LLM’s uninterpretable nature and the extreme flexibility of free-form explanations, several challenges remain: such as struggling with inaccurate reasoning, hallucinations, and not aligning with human preferences. In this talk, we will focus on (1) our design of leveraging structured information (that is grounded to the context), for the explainable complex question answering and reasoning; (2) our multi-module interpretable framework for inductive reasoning, which conducts step-wise faithful reasoning with iterative feedback.
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World Tree: A corpus of explanation graphs for elementary science questions supporting multi-hop inference
Developing methods of automated inference that are able to provide users with compelling human-readable justifications for why the answer to a question is correct is critical for domains such as science and medicine, where user trust and detecting costly errors are limiting factors to adoption. One of the central barriers to training question answering models on explainable inference tasks is the lack of gold explanations to serve as training data. In this paper we present a corpus of explanations for standardized science exams, a recent challenge task for question answering. We manually construct a corpus of detailed explanations for nearly all publicly available standardized elementary science question (approximately 1,680 3 rd through 5 th grade questions) and represent these as “explanation graphs” - sets of lexically overlapping sentences that describe how to arrive at the correct answer to a question through a combination of domain and world knowledge. We also provide an explanation-centered tablestore, a collection of semi-structured tables that contain the knowledge to construct these elementary science explanations. Together, these two knowledge resources map out a substantial portion of the knowledge required for answering and explaining elementary science exams, and provide both structured and free-text training data for the explainable inference task.
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- Award ID(s):
- 1740858
- PAR ID:
- 10111800
- Date Published:
- Journal Name:
- LREC 2018 - 11th International Conference on Language Resources and Evaluation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
