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- RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style192
RM-Bench is introduced, a novel benchmark designed to evaluate reward models based on their sensitivity to subtle content differences and resistance to style biases, and it is revealed that even state-of-the-art models achieve an average performance, which falls short of random-level accuracy when faced with style bias interference.
- KoLA: Carefully Benchmarking World Knowledge of Large Language Models96
A Knowledge-oriented LLM Assessment benchmark (KoLA), in which a contrastive system is adopted for better numerical comparability across tasks and models and a unique self-contrast metric for automatically evaluating knowledge-creating ability is adopted.
- T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling87
It is demonstrated that T1 with open LLMs as its base exhibits inference scaling behavior and achieves superior performance on challenging math reasoning benchmarks and a simple strategy to examine inference scaling, where increased inference budgets directly lead to T1's better performance without any additional verification.
- MOOCCubeX: A Large Knowledge-centered Repository for Adaptive Learning in MOOCs78
This work constructs MOOCCubeX, a large, knowledge-centered repository consisting of 4,216 courses, 230,263 videos, 358,265 exercises, 637,572 fine-grained concepts and over 296 million behavioral data of 3,330,294 students, for supporting the research topics on adaptive learning in MOOCs.
- Program Transfer for Answering Complex Questions over Knowledge Bases70
This paper designs a novel two-stage parsing framework with an efficient ontology-guided pruning strategy that aims to leverage the valuable program annotations on the rich-resourced KBs as external supervision signals to aid program induction for the low-resourcing KBs that lack program annotations.
- Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks39
This survey probes the core challenges that the rise of LLMs poses for evaluation, and identifies and analyzes two pivotal transitions: from task-specific to capability-based evaluation and from manual to automated evaluation.
- MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs34
This paper presents MoocRadar, a fine-grained, multi-aspect knowledge repository consisting of 2,513 exercise questions, 5,600 knowledge concepts, and over 12 million behavioral records, and proposes a framework to guarantee a high-quality and comprehensive annotation of fine- grained concepts and cognitive labels.
- LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking32
LLM-Augmented Entity Linking (LLMAEL) is introduced, the first framework to enhance specialized EL models with LLM data augmentation, which leverages off-the-shelf, tuning-free LLMs as context augmenters, generating entity descriptions to serve as additional input for specialized EL models.
- GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation24
Comprehensive evaluations from automatic to human perspective demonstrate the advantages of GLM-Dialog comparing with existing open source Chinese dialogue models and offer the evaluation platform online in an effort to prompt the development of open source models and reliable dialogue evaluation systems.
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- LittleMu: Deploying an Online Virtual Teaching Assistant via Heterogeneous Sources Integration and Chain of Teach Prompts21
A virtual MOOC teaching assistant, LittleMu with minimum labeled training data, to provide question answering and chit-chat services on XuetangX MOOC platform, which continuously contributes to a more convenient and fair education.
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- How do Transformers Learn Implicit Reasoning?17
This paper studies how implicit multi-hop reasoning emerges by training transformers from scratch in a controlled symbolic environment, revealing a three-stage developmental trajectory: early memorization, followed by in-distribution generalization, and eventually cross-distribution generalization.
- A Roadmap for Big Model17
This paper discusses not only the BM technologies themselves but also the prerequisites for BM training and applications with BMs, dividing the BM review into four parts: Resource, Models, Key Technologies and Application.
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- SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking14
SoAy enables LLMs to generate code for invoking APIs, guided by a pre-constructed API calling sequence referred to as a solution, which simplifies the model's understanding of complex API relationships, while the generated code enhances reasoning efficiency.
- AtomR: Atomic Operator-Empowered Large Language Models for Heterogeneous Knowledge Reasoning14
AtomR is introduced, a framework for LLMs to conduct accurate heterogeneous knowledge reasoning at the atomic level and three atomic knowledge operators are proposed, a unified set of operators for LLMs to retrieve and manipulate knowledge from heterogeneous sources.
- Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making14
A novel EM framework that consists of Heterogeneous Information Fusion (HIF) and Key Attribute Tree (KAT) Induction to decouple feature representation from matching decision and is highly efficient and outperforms SOTA EM models in most cases.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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