Arindam Mitra

ICLR Conference 2024 Conference Paper

Teaching Language Models to Hallucinate Less with Synthetic Tasks

• Erik Jones
• Hamid Palangi
• Clarisse Simões
• Varun Chandrasekaran
• Subhabrata Mukherjee
• Arindam Mitra
• Ahmed Hassan Awadallah
• Ece Kamar

Large language models (LLMs) frequently hallucinate on abstractive summarization tasks such as document-based question-answering, meeting summarization, and clinical report generation, even though all necessary information is included in context. However, optimizing to make LLMs hallucinate less is challenging, as hallucination is hard to efficiently, cheaply, and reliably evaluate at each optimization step. In this work, we show that reducing hallucination on a _synthetic task_ can also reduce hallucination on real-world downstream tasks. Our method, SynTra, first designs a synthetic task where hallucinations are easy to elicit and measure. It next optimizes the LLM's system message via prefix tuning on the synthetic task, then uses the system message on realistic, hard-to-optimize tasks. Across three realistic abstractive summarization tasks, we reduce hallucination for two 13B-parameter LLMs using supervision signal from only a synthetic retrieval task. We also find that optimizing the system message rather than the model weights can be critical; fine-tuning the entire model on the synthetic task can counterintuitively _increase_ hallucination. Overall, SynTra demonstrates that the extra flexibility of working with synthetic data can help mitigate undesired behaviors in practice.

AAAI Conference 2022 Conference Paper

Improving Biomedical Information Retrieval with Neural Retrievers

• Man Luo
• Arindam Mitra
• Tejas Gokhale
• Chitta Baral

Information retrieval (IR) is essential in search engines and dialogue systems as well as natural language processing tasks such as open-domain question answering. IR serve an important function in the biomedical domain, where content and sources of scientific knowledge may evolve rapidly. Although neural retrievers have surpassed traditional IR approaches such as TF-IDF and BM25 in standard open-domain question answering tasks, they are still found lacking in the biomedical domain. In this paper, we seek to improve information retrieval (IR) using neural retrievers (NR) in the biomedical domain, and achieve this goal using a three-pronged approach. First, to tackle the relative lack of data in the biomedical domain, we propose a template-based question generation method that can be leveraged to train neural retriever models. Second, we develop two novel pre-training tasks that are closely aligned to the downstream task of information retrieval. Third, we introduce the “Poly-DPR” model which encodes each context into multiple context vectors. Extensive experiments and analysis on the BioASQ challenge suggest that our proposed method leads to large gains over existing neural approaches and beats BM25 in the small-corpus setting. We show that BM25 and our method can complement each other, and a simple hybrid model leads to further gains in the large corpus setting.

AAAI Conference 2020 Conference Paper

Enhancing Natural Language Inference Using New and Expanded Training Data Sets and New Learning Models

• Arindam Mitra
• Ishan Shrivastava
• Chitta Baral

Natural Language Inference (NLI) plays an important role in many natural language processing tasks such as question answering. However, existing NLI modules that are trained on existing NLI datasets have several drawbacks. For example, they do not capture the notion of entity and role well and often end up making mistakes such as “Peter signed a deal” can be inferred from “John signed a deal”. As part of this work, we have developed two datasets that help mitigate such issues and make the systems better at understanding the notion of “entities” and “roles”. After training the existing models on the new dataset we observe that the existing models do not perform well on one of the new benchmark. We then propose a modification to the “word-to-word” attention function which has been uniformly reused across several popular NLI architectures. The resulting models perform as well as their unmodified counterparts on the existing benchmarks and perform significantly well on the new benchmarks that emphasize “roles” and “entities”.

AAAI Conference 2019 Conference Paper

Declarative Question Answering over Knowledge Bases Containing Natural Language Text with Answer Set Programming

• Arindam Mitra
• Peter Clark
• Oyvind Tafjord
• Chitta Baral

While in recent years machine learning (ML) based approaches have been the popular approach in developing endto-end question answering systems, such systems often struggle when additional knowledge is needed to correctly answer the questions. Proposed alternatives involve translating the question and the natural language text to a logical representation and then use logical reasoning. However, this alternative falters when the size of the text gets bigger. To address this we propose an approach that does logical reasoning over premises written in natural language text. The proposed method uses recent features of Answer Set Programming (ASP) to call external NLP modules (which may be based on ML) which perform simple textual entailment. To test our approach we develop a corpus based on the life cycle questions and showed that Our system achieves up to 18% performance gain when compared to standard MCQ solvers.

KR Conference 2018 Short Paper

Knowledge Representation and Reasoning in Answering Science Questions: A Case Study for Food Web Questions

• Arindam Mitra
• Chitta Baral
• Peter Clark

Question Type 1: Explain how a perturbation leads to a A group of researchers from the Allen Institute of Artificial Intelligence has proposed the Aristo challenge that requires answering science questions. The goal of the challenge is to aid in the development of machines that can understand natural language, use knowledge and reason. In this work, we take a subset of those questions, namely the questions from the chapters of food web. We model a consequence operator for the food webs that given a food web and a perturbation to some of the populations aims to compute possible effects on the other populations in the food web. We then use this operator to answers questions of the kind, ‘Explain why the population of rabbits might decrease if the population of mice decreased. ’ or ‘Explain why the population of rabbits might change if the population of mice decreased. ’ Unlike the previous works which deal with only direct predator-prey situations, here we aim to characterize the effect(s) even when the two populations in the question are indirectly related.

AAAI Conference 2016 Conference Paper

Addressing a Question Answering Challenge by Combining Statistical Methods with Inductive Rule Learning and Reasoning

• Arindam Mitra
• Chitta Baral