Papers Open-Ended Question Answering
“Open-Ended Question Answering” 태그가 달린 논문 796편 · 필터 해제
WIP: Large Language Model-Enhanced Smart Tutor for Undergraduate Circuit Analysis
This research-to-practice work-in-progress (WIP) paper presents an AI-enabled smart tutor designed to provide homework assessment and feedback for students in an undergraduate circuit analysis course. We detail the tutor…
Language ModelingLanguage ModellingLarge Language ModelOpen-Ended Question Answering+2VersaVid-R1: A Versatile Video Understanding and Reasoning Model from Question Answering to Captioning Tasks
Recent advancements in multimodal large language models have successfully extended the Reason-Then-Respond paradigm to image-based reasoning, yet video-based reasoning remains an underdeveloped frontier, primarily due to…
Multiple-choiceOpen-Ended Question AnsweringQuestion AnsweringVideo Captioning+1anyECG-chat: A Generalist ECG-MLLM for Flexible ECG Input and Multi-Task Understanding
The advent of multimodal large language models (MLLMs) has sparked interest in their application to electrocardiogram (ECG) analysis. However, existing ECG-focused MLLMs primarily focus on report generation tasks, often …
Open-Ended Question AnsweringQuestion AnsweringCulFiT: A Fine-grained Cultural-aware LLM Training Paradigm via Multilingual Critique Data Synthesis
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, yet they often exhibit a specific cultural biases, neglecting the values and linguistic diversity of low-resource regions. This…
DiversityOpen-Ended Question AnsweringQuestion AnsweringO$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering
Large Language Models (LLMs), despite their advancements, are fundamentally limited by their static parametric knowledge, hindering performance on tasks requiring open-domain up-to-date information. While enabling LLMs t…
Answer GenerationOpen-Ended Question AnsweringQuestion AnsweringWorld KnowledgeTinyRS-R1: Compact Multimodal Language Model for Remote Sensing
Remote-sensing applications often run on edge hardware that cannot host today's 7B-parameter multimodal language models. This paper introduces TinyRS, the first 2B-parameter multimodal small language model (MSLM) optimiz…
Language ModelingLanguage ModellingOpen-Ended Question AnsweringQuestion Answering+4Ranked Voting based Self-Consistency of Large Language Models
Majority voting is considered an effective method to enhance chain-of-thought reasoning, as it selects the answer with the highest "self-consistency" among different reasoning paths (Wang et al., 2023). However, previous…
Multiple-choiceOpen-Ended Question AnsweringQuestion AnsweringVLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making
Recent research looks to harness the general knowledge and reasoning of large language models (LLMs) into agents that accomplish user-specified goals in interactive environments. Vision-language models (VLMs) extend LLMs…
Decision MakingGeneral KnowledgeOpen-Ended Question AnsweringQ-Learning+3Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Reliable Response Generation in the Wild
The proliferation of large language models (LLMs) has significantly advanced information retrieval systems, particularly in response generation (RG). Unfortunately, LLMs often face knowledge conflicts between internal me…
Decision MakingInformation RetrievalMisinformationNavigate+6AutoDrive-QA- Automated Generation of Multiple-Choice Questions for Autonomous Driving Datasets Using Large Vision-Language Models
In autonomous driving, open-ended question answering often suffers from unreliable evaluations because freeform responses require either complex metrics or subjective human judgment. To address this challenge, we introdu…
Autonomous DrivingMultiple-choiceOpen-Ended Question AnsweringPrediction+1FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users
Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curation. Inspired by the strong in-context learning capabilities of LLMs, we propos…
In-Context LearningMeta-LearningOpen-Ended Question AnsweringQuestion AnsweringTime-MQA: Time Series Multi-Task Question Answering with Context Enhancement
Time series data are foundational in finance, healthcare, and energy domains. However, most existing methods and datasets remain focused on a narrow spectrum of tasks, such as forecasting or anomaly detection. To bridge …
Anomaly DetectionNatural Language QueriesOpen-Ended Question AnsweringQuestion Answering+1PRIV-QA: Privacy-Preserving Question Answering for Cloud Large Language Models
The rapid development of large language models (LLMs) is redefining the landscape of human-computer interaction, and their integration into various user-service applications is becoming increasingly prevalent. However, t…
Open-Ended Question AnsweringPrivacy PreservingQuestion AnsweringNeptune: The Long Orbit to Benchmarking Long Video Understanding
We introduce Neptune, a benchmark for long video understanding that requires reasoning over long time horizons and across different modalities. Many existing video datasets and models are focused on short clips (10s-30s)…
BenchmarkingMultimodal ReasoningMultiple-choiceOpen-Ended Question Answering+2Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study over Open-ended Question Answering
Recent works integrating Knowledge Graphs (KGs) have led to promising improvements in enhancing reasoning accuracy of Large Language Models (LLMs). However, current benchmarks focus mainly on closed-ended tasks, leaving …
HallucinationKnowledge GraphsOpen-Ended Question AnsweringQuestion AnsweringTVBench: Redesigning Video-Language Evaluation
Large language models have demonstrated impressive performance when integrated with vision models even enabling video understanding. However, evaluating these video models presents its own unique challenges, for which se…
Multiple-choiceOpen-Ended Question AnsweringQuestion AnsweringVideo Understanding+2Utilize the Flow before Stepping into the Same River Twice: Certainty Represented Knowledge Flow for Refusal-Aware Instruction Tuning
Refusal-Aware Instruction Tuning (RAIT) enables Large Language Models (LLMs) to refuse to answer unknown questions. By modifying responses of unknown questions in the training data to refusal responses such as "I don't k…
HallucinationMultiple-choiceOpen-Ended Question AnsweringQuestion AnsweringVideo Instruction Tuning With Synthetic Data
The development of video large multimodal models (LMMs) has been hindered by the difficulty of curating large amounts of high-quality raw data from the web. To address this, we propose an alternative approach by creating…
3D Question Answering (3D-QA)Instruction FollowingMultiple-choice+5CamelEval: Advancing Culturally Aligned Arabic Language Models and Benchmarks
Large Language Models (LLMs) are the cornerstones of modern artificial intelligence systems. This paper introduces Juhaina, a Arabic-English bilingual LLM specifically designed to align with the values and preferences of…
Instruction FollowingOpen-Ended Question AnsweringQuestion AnsweringRanking Generated Answers: On the Agreement of Retrieval Models with Humans on Consumer Health Questions
Evaluating the output of generative large language models (LLMs) is challenging and difficult to scale. Many evaluations of LLMs focus on tasks such as single-choice question-answering or text classification. These tasks…
Open-Ended Question AnsweringQuestion AnsweringRetrievalSingle Choice Question+2