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Found 24 papers, 17 papers with code
ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation
This process iterates until a Finish action is chosen.
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
This paper introduces LongBench v2, a benchmark designed to assess the ability of LLMs to handle long-context problems requiring deep understanding and reasoning across real-world multitasks.
AtomR: Atomic Operator-Empowered Large Language Models for Heterogeneous Knowledge Reasoning
Drawing inspiration from the graph modeling of knowledge, AtomR leverages large language models (LLMs) to decompose complex questions into combinations of three atomic knowledge operators, significantly enhancing the reasoning process at both the planning and execution stages.
LongReward: Improving Long-context Large Language Models with AI Feedback
Though significant advancements have been achieved in developing long-context large language models (LLMs), the compromised quality of LLM-synthesized data for supervised fine-tuning (SFT) often affects the long-context performance of SFT models and leads to inherent limitations.
LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA
Though current long-context large language models (LLMs) have demonstrated impressive capacities in answering user questions based on extensive text, the lack of citations in their responses makes user verification difficult, leading to concerns about their trustworthiness due to their potential hallucinations.
Aligning Teacher with Student Preferences for Tailored Training Data Generation
Thus, we propose ARTE, dubbed Aligning TeacheR with StudenT PreferencEs, a framework that aligns the teacher model with student preferences to generate tailored training examples for Knowledge Distillation.
SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation
This paper introduces Self-aware Knowledge Retrieval (SeaKR), a novel adaptive RAG model that extracts self-aware uncertainty of LLMs from their internal states.
Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models
However, knowledge in the document may conflict with the memory of LLMs due to outdated or incorrect knowledge in the LLMs' parameters.
KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge Bases
Secondly, KB-Plugin utilizes abundant annotated data from a rich-resourced KB to train another pluggable module, namely PI plugin, which can help the LLM extract question-relevant schema information from the schema plugin of any KB and utilize this information to induce programs over this KB.
How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering
A typical approach to KBQA is semantic parsing, which translates a question into an executable logical form in a formal language.
Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions
During reasoning, for leaf nodes, LLMs choose a more confident answer from Closed-book QA that employs parametric knowledge and Open-book QA that employs retrieved external knowledge, thus eliminating the negative retrieval problem.
KoRC: Knowledge oriented Reading Comprehension Benchmark for Deep Text Understanding
However, these benchmarks have encountered two major limitations.
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering
We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries.
FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering
The generalization problem on KBQA has drawn considerable attention.
KoLA: Carefully Benchmarking World Knowledge of Large Language Models
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations.
Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering
To facilitate reasoning, we propose a novel two-stage XQA framework, Reasoning over Hierarchical Question Decomposition Tree (RoHT).
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation
Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and output syntax.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Schema-Free Dependency Parsing via Sequence Generation
To remedy these drawbacks, we propose to achieve universal and schema-free Dependency Parsing (DP) via Sequence Generation (SG) DPSG by utilizing only the pre-trained language model (PLM) without any auxiliary structures or parsing algorithms.
Program Transfer for Answering Complex Questions over Knowledge Bases
In this paper, we propose the approach of program transfer, which aims to leverage the valuable program annotations on the rich-resourced KBs as external supervision signals to aid program induction for the low-resourced KBs that lack program annotations.
TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph
Multi-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer.
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base
To this end, we introduce KQA Pro, a dataset for Complex KBQA including ~120K diverse natural language questions.
OpenKE: An Open Toolkit for Knowledge Embedding
We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space.
