Papers with answer prediction
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to QA using retrieval-augmented knowledge are limited by limited coverage and noisy information. |
| Approach: | They propose an induction-augmented generation framework that utilizes inductive knowledge along with retrieved documents for implicit reasoning. |
| Outcome: | The proposed framework outperforms RAG and ChatGPT on two Open-Domain QA tasks. |
Explainable Inference Over Grounding-Abstract Chains for Science Questions (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing inference models for science questions are black-box by nature, lacking explanations for their predictions. |
| Approach: | They propose an explainable inference approach for science questions by reasoning on grounding and abstract inference chains. |
| Outcome: | The proposed model generates plausible explanations for science questions using a weighted graph of relevant facts and a Bayesian Optimisation formalism. |
Read and Comprehend by Gated-Attention Reader with More Belief (N18-4)
Copied to clipboard
| Challenge: | Existing approaches to read comprehension using gated-attention have been effective . collaborative gating and self-belief aggregation are proposed to address these assumptions . |
| Approach: | They propose to use a document-to-query attention system to gate token encodings of a query . they conjecture that query tokens other than the cloze token may be informative . |
| Outcome: | The proposed approaches advance the state-of-the-art results in CNN, Daily Mail, and Who Did What public test sets. |
PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs (2024.starsem-1)
Copied to clipboard
| Challenge: | Existing approaches to utilizing explicit knowledge graphs (KGs) are limited by the number of nodes in the subgraph. |
| Approach: | They propose a grounding-pruning-reasoning pipeline to prune noisy nodes in subgraphs to improve the efficiency of graph reasoning with KG. |
| Outcome: | The proposed method reduces computation cost and memory usage while obtaining decent representation of pruned subgraphs. |
KAT: A Knowledge Augmented Transformer for Vision-and-Language (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing methods for knowledge retrieval and answer prediction have left open questions about the quality and relevance of the retrieved knowledge and how the reasoning processes over implicit and explicit knowledge should be integrated. |
| Approach: | They propose a Knowledge Augmented Transformer which integrates both implicit and explicit knowledge in an encoder-decoder architecture while simultaneously reasoning over both knowledge sources during answer generation. |
| Outcome: | The proposed model achieves a strong state-of-the-art (+6% absolute) on the open-domain multimodal task of OK-VQA. |
Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing studies have utilized underlying reasoning (UR) tasks in multi-hop question answering datasets to explain the predicted answers and evaluate models' reasoning abilities. |
| Approach: | They analyze UR tasks in QA datasets to determine their effectiveness . they find that UR task is helpful in preventing reasoning shortcuts . |
| Outcome: | The proposed model improves QA performance, reasoning shortcuts, and robustness on adversarial questions. |
R3 Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have evaluated LLMs under noise-free context but the dilemma for LLM to produce inaccurate results under noisy context has not been fully investigated. |
| Approach: | They propose a new method for CoT reasoning using Chain-of-Thought prompting that interacts with LLMs to perform key sentence extraction, variable declaration and answer prediction. |
| Outcome: | The proposed method outperforms existing CoT prompting methods on five reasoning tasks under noisy context. |
Unveiling Confirmation Bias in Chain-of-Thought Reasoning (2025.findings-acl)
Copied to clipboard
| Challenge: | Chain-of-thought (CoT) prompting has been widely adopted to enhance the reasoning capabilities of large language models (LLMs). |
| Approach: | They propose to examine how internal beliefs affect reasoning generation and reasoning-guided answer prediction in CoT by decomposing CoT into a two-stage process. |
| Outcome: | The proposed model beliefs affect reasoning generation and reasoning-guided answer prediction in CoT, and the results provide strong evidence of confirmation bias in LLMs. |
Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs (2020.coling-main)
Copied to clipboard
| Challenge: | Using conceptual graphs, we can embed a sentence into a knowledge embedding in a graph to solve slot-filling challenges in question answering and capture neighbouring connections of reference concept nodes. |
| Approach: | They propose a miscellaneous-context-based method to embed a sentence into a directed graph and a task-agnostic semantics module which integrates the syntactic-semantic information. |
| Outcome: | The proposed method can accommodate slot-filling challenges in typical question answering and capture the neighbouring connections of reference concept nodes. |
A Robust Dual-debiasing VQA Model based on Counterfactual Causal Effect (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing VQA models suffer from language bias that indicates a spurious correlation between textual questions and answers. |
| Approach: | They propose a model agnostic dual-debiasing framework that models two types of language bias by separate branches under counterfactual inference framework. |
| Outcome: | The proposed framework significantly reduces language bias and achieves state-of-the-art performance on the benchmark datasets. |
Span Selection Pre-training for Question Answering (2020.acl-main)
Copied to clipboard
Michael Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto, Lin Pan, G P Shrivatsa Bhargav, Dinesh Garg, Avi Sil
| Challenge: | Pre-trained BERTs provide large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA). |
| Approach: | They propose a new pre-training task inspired by reading comprehension to better align the pre- training from memorization to understanding. |
| Outcome: | The proposed model outperforms BERT-BASE and BERT LARGE on a new dataset and improves answer prediction F1 by 4 points and supporting fact prediction F1. |
Towards Interpreting BERT for Reading Comprehension Based QA (2020.emnlp-main)
Copied to clipboard
| Challenge: | Pretrained language models such as ELMO and XLNet have achieved state-of-the-art performance on various NLP tasks. |
| Approach: | They propose to define a layer’s role or functionality using Integrated Gradients and perform preliminary analysis across all layers. |
| Outcome: | The proposed model performs better than existing models on RCQA and ELMO, but it lacks the human-level performance needed to perform the task. |
Benchmarking Large Language Models on CFLUE - A Chinese Financial Language Understanding Evaluation Dataset (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in large language models have revolutionized natural language processing (NLP) there is an urgent need for new benchmarks to keep pace with the development of LLMs. |
| Approach: | They propose a benchmark to assess the capability of large language models (LLMs) they use a dataset to provide both knowledge assessment and application assessment . |
| Outcome: | The proposed benchmark provides datasets tailored for knowledge assessment and application assessment. |
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering (2022.acl-long)
Copied to clipboard
Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng
| Challenge: | Open-Domain Question Answering (ODQA) models typically include a retrieving module and a reading module. |
| Approach: | They propose a new open-domain question-answering framework that uses a knowledge-enhanced version of FiD to improve the approach. |
| Outcome: | The proposed model improves on ODQA benchmark datasets with less than 40% computation cost. |
Robustifying Multi-hop QA through Pseudo-Evidentiality Training (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to robustify multi-hop question answering models require expensive annotations. |
| Approach: | They propose a method to supervise answers with right reasoning chains without annotations . they compare answers confidence with and without evidence sentences to generate "pseudo-evidentiality" annotations. |
| Outcome: | The proposed model is accurate and robust in multi-hop reasoning. |
Lite Unified Modeling for Discriminative Reading Comprehension (2022.acl-long)
Copied to clipboard
| Challenge: | generative and discriminative MRCs focus on answer generation, extractive MRC on answer extraction. |
| Approach: | They propose a lightweight POS-Enhanced Iterative Co-Attention Network to handle diverse discriminative MRC tasks synchronously. |
| Outcome: | The proposed model improves on four discriminative MRC benchmarks. |
Hierarchical Graph Network for Multi-hop Question Answering (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing multi-hop question answering models focus on multi-level reasoning across multiple documents or paragraphs. |
| Approach: | They propose a hierarchical graph network that aggregates clues from scattered texts . they use a set of contextual encoders to initialize nodes on different levels of granularity . |
| Outcome: | The proposed model outperforms existing multi-hop QA approaches on the HotpotQA benchmark. |
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing adversarial models rely on keyword matching and ignore relevant contextual relations for answer prediction. |
| Approach: | They propose to use keyword matching to attack model with two biases that rely on a perturbed answer sentence and a distracting answer sentence to misguide model. |
| Outcome: | The proposed method produces fluent and grammatical adversarial contexts while maintaining gold answers. |
Graph-Guided Textual Explanation Generation Framework (2025.emnlp-main)
Copied to clipboard
Shuzhou Yuan, Jingyi Sun, Ran Zhang, Michael Färber, Steffen Eger, Pepa Atanasova, Isabelle Augenstein
| Challenge: | Existing work has questioned their faithfulness, as they may not accurately reflect the model’s internal reasoning process regarding its predicted answer. |
| Approach: | They propose a Graph-Guided Textual Explanation Generation framework that generates a graph neural network layer that guides the NLE generation and generates explanations with greater semantic and lexical similarity to human-written ones. |
| Outcome: | The proposed framework improves NLE faithfulness by up to 12.12% compared to baseline methods on encoder-decoder and decoder-only models. |