Papers with multi-choice question answering
HEAD-QA: A Healthcare Dataset for Complex Reasoning (P19-1)
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| Challenge: | Recent progress in question answering has been led by neural models, but current methods are too data intensive and weak. |
| Approach: | They propose a multi-choice question answering testbed to encourage research on complex reasoning. |
| Outcome: | The proposed dataset is useful as a benchmark for future work. |
Winnowing Knowledge for Multi-choice Question Answering (2021.findings-emnlp)
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| Challenge: | Existing reasoning models suffer from noises in retrieved knowledge . encoding methods that use commonsense knowledge are less effective . |
| Approach: | They propose a method which conducts interception and soft filtering to reduce noise . they use commonsense knowledge from Wikipedia and ConceptNet to encode questions and options . |
| Outcome: | The proposed method improves on commonsense question answering tasks compared to baselines . it is able to conduct interception and soft filtering to shield the encoder from noise . |
An Investigation of LLMs’ Inefficacy in Understanding Converse Relations (2023.emnlp-main)
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| Challenge: | Existing benchmarks for Large Language Models (LLMs) follow the data distribution of pre-training data. |
| Approach: | They propose a benchmark ConvRe focusing on converse relations which contains 17 relations and 1240 triples extracted from popular knowledge graph completion datasets. |
| Outcome: | The proposed benchmark focuses on converse relations, which contains 17 relations and 1240 triples extracted from popular knowledge graph completion datasets. |
Warmup Generations: A Task-Agnostic Approach for Guiding Sequence-to-Sequence Learning with Unsupervised Initial State Generation (2025.acl-long)
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| Challenge: | Existing supervised fine-tuning (SFT) methods focus on directly generating the target output without leveraging the benefits of intermediate steps or initial guidance. |
| Approach: | They propose a task-agnostic framework that enables models to generate intermediate "warmup" sequences that are iteratively refined to maximize their contribution to the final output. |
| Outcome: | The proposed framework outperforms traditional supervised fine-tuning methods on translation, summarization, and multi-choice question answering tasks. |
Measuring Chain of Thought Faithfulness by Unlearning Reasoning Steps (2025.emnlp-main)
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| Challenge: | Language models (LMs) produce a chain of thought (CoT) when prompted to think step-by-step, but it is unclear whether the reasoning encoded in the CoT is faithful to the models’ parametric beliefs. |
| Approach: | They propose a framework for measuring parametric faithfulness of generated reasoning by unlearning reasoning steps (FUR) they propose to erase information contained in reasoning steps from model parameters and measure faithfulness as the resulting effect on the model’s prediction. |
| Outcome: | The proposed framework erases information contained in reasoning steps from model parameters and measures faithfulness as the resulting effect on the model’s prediction. |
Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering (2022.emnlp-main)
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| Challenge: | Existing approaches to multi-choice question answering are based on binary classifications instead of scoring each answer as a single class. |
| Approach: | They propose a simple refactoring of multi-choice question answering tasks as a series of binary classifications and propose re-framing to make them more efficient. |
| Outcome: | The proposed approach is significantly more effective across different tasks and models. |
Evaluating Robustness of Large Language Models Against Multilingual Typographical Errors (2026.acl-long)
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| Challenge: | Large language models (LLMs) are increasingly deployed in multilingual, real-world applications where user inputs introduce typographical errors. |
| Approach: | They propose a multilingual typo generation algorithm that simulates human-like errors based on language-specific keyboard layouts and typing behavior. |
| Outcome: | The proposed model can generate the correct answer ("500") under typos in English, German, and Russian. |
Intrinsic Self-correction for Enhanced Morality: An Analysis of Internal Mechanisms and the Superficial Hypothesis (2024.emnlp-main)
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| Challenge: | Existing studies on the effectiveness of moral self-correction in large language models have not been conducted. |
| Approach: | They propose that moral self-correction is a computationally efficient method for reducing harmful content in LLMs. |
| Outcome: | The proposed method reduces harmful content in LLMs, but it remains under-explored . it can help LLM find shortcut to more morally correct output, the authors argue . |
Fine-Tuned Thoughts: Leveraging Chain-of-Thought Reasoning for Industrial Asset Health Monitoring (2025.findings-emnlp)
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| Challenge: | Small Language Models (SLMs) are becoming increasingly popular in specialized fields such as industrial applications. |
| Approach: | They propose a framework which transfers reasoning capabilities via Chain-of-Thought distillation from Large Language Models (LLMs) to smaller, more efficient models (SLMs) |
| Outcome: | The proposed framework outperforms the base models in Industry 4.0 by a significant margin. |