Science Question Answering
14 papers with code • 1 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Science Question Answering
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
ScienceQA
FrenchMedMCQA
SemOpenAlex
BioFuelQR
Most implemented papers
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
pku-yuangroup/chat-univi
•
•
CVPR 2024
Large language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multimodal conversations.
Multimodal Chain-of-Thought Reasoning in Language Models
Experimental results on ScienceQA and A-OKVQA benchmark datasets show the effectiveness of our proposed approach.
Towards CausalGPT: A Multi-Agent Approach for Faithful Knowledge Reasoning via Promoting Causal Consistency in LLMs
Drawing inspiration from the orchestration of diverse specialized agents collaborating to tackle intricate tasks, we propose a framework named Causal-Consistency Chain-of-Thought (CaCo-CoT) that harnesses multi-agent collaboration to bolster the faithfulness and causality of foundation models, involving a set of reasoners and evaluators.
Unification-based Reconstruction of Multi-hop Explanations for Science Questions
This paper presents a novel framework for reconstructing multi-hop explanations in science Question Answering (QA).
Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering
wwxu21/AMR-SG
•
•
Findings (ACL) 2021
Our framework contains three new ideas: (a) {\tt AMR-SG}, an AMR-based Semantic Graph, constructed by candidate fact AMRs to uncover any hop relations among question, answer and multiple facts.
Exploiting Reasoning Chains for Multi-hop Science Question Answering
wwxu21/cgr
•
•
Findings (EMNLP) 2021
We propose a novel Chain Guided Retriever-reader ({\tt CGR}) framework to model the reasoning chain for multi-hop Science Question Answering.
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
We further design language models to learn to generate lectures and explanations as the chain of thought (CoT) to mimic the multi-hop reasoning process when answering ScienceQA questions.
Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering
declare-lab/team
•
•
We show the efficacy of our proposed approach in different tasks -- abductive reasoning, commonsense question answering, science question answering, and sentence completion.
T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Mixed Large Language Model Signals for Science Question Answering
t-sciq/t-sciq
•
•
To address these issues, we propose a novel method termed T-SciQ that aims at teaching science question answering with LLM signals.
Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models
luogen1996/lavin
•
•
NeurIPS 2023
To validate MMA, we apply it to a recent LLM called LLaMA and term this formed large vision-language instructed model as LaVIN.
