Search Results for author:
Found 73 papers, 43 papers with code
ISCAS at SemEval-2022 Task 10: An Extraction-Validation Pipeline for Structured Sentiment Analysis
ISCAS participated in both sub-tasks in SemEval-2022 Task 10: Structured Sentiment competition.
CATAMARAN: A Cross-lingual Long Text Abstractive Summarization Dataset
Cross-lingual summarization, which produces the summary in one language from a given source document in another language, could be extremely helpful for humans to obtain information across the world.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) in many knowledge-based tasks.
Multi-Facet Counterfactual Learning for Content Quality Evaluation
Evaluating the quality of documents is essential for filtering valuable content from the current massive amount of information.
Seg2Act: Global Context-aware Action Generation for Document Logical Structuring
Document logical structuring aims to extract the underlying hierarchical structure of documents, which is crucial for document intelligence.
Rethinking Reward Model Evaluation: Are We Barking up the Wrong Tree?
Although this method is straightforward and widely adopted, the relationship between RM accuracy and downstream policy performance remains under-explored.
READoc: A Unified Benchmark for Realistic Document Structured Extraction
Document Structured Extraction (DSE) aims to extract structured content from raw documents.
Critic-CoT: Boosting the reasoning abilities of large language model via Chain-of-thoughts Critic
Self-critic has become a crucial mechanism for enhancing the reasoning performance of LLMs.
CRUXEval-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution
However, there is an unignorable programming language bias in existing code benchmarks -- over 95% code generation benchmarks are dominated by Python, leaving the LLMs' capabilities in other programming languages such as Java and C/C++ unknown.
DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation
We notice that LLMs are generally good at computation tasks while falling short on cryptography and system coding tasks.
REInstruct: Building Instruction Data from Unlabeled Corpus
Manually annotating instruction data for large language models is difficult, costly, and hard to scale.
StructEval: Deepen and Broaden Large Language Model Assessment via Structured Evaluation
Evaluation is the baton for the development of large language models.
Beyond Correctness: Benchmarking Multi-dimensional Code Generation for Large Language Models
In recent years, researchers have proposed numerous benchmarks to evaluate the impressive coding capabilities of large language models (LLMs).
On-Policy Fine-grained Knowledge Feedback for Hallucination Mitigation
Previous learning-based methods focus on detecting knowledge boundaries and finetuning models with instance-level feedback, but they suffer from inaccurate signals due to off-policy data sampling and coarse-grained feedback.
Open Grounded Planning: Challenges and Benchmark Construction
In this paper, we propose a new planning task--open grounded planning.
Towards Scalable Automated Alignment of LLMs: A Survey
Alignment is the most critical step in building large language models (LLMs) that meet human needs.
Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching
Based on our findings, we further design a compound entity matching framework (ComEM) that leverages the composition of multiple strategies and LLMs.
Base of RoPE Bounds Context Length
We revisit the role of RoPE in LLMs and propose a novel property of long-term decay, we derive that the \textit{base of RoPE bounds context length}: there is an absolute lower bound for the base value to obtain certain context length capability.
URL: Universal Referential Knowledge Linking via Task-instructed Representation Compression
Linking a claim to grounded references is a critical ability to fulfill human demands for authentic and reliable information.
Towards Universal Dense Blocking for Entity Resolution
Blocking is a critical step in entity resolution, and the emergence of neural network-based representation models has led to the development of dense blocking as a promising approach for exploring deep semantics in blocking.
Spiral of Silence: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering
The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent.
Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation
In this paper, we propose Credibility-aware Generation (CAG), a universally applicable framework designed to mitigate the impact of flawed information in RAG.
Few-shot Named Entity Recognition via Superposition Concept Discrimination
Then a superposition instance retriever is applied to retrieve corresponding instances of these superposition concepts from large-scale text corpus.
Meta-Cognitive Analysis: Evaluating Declarative and Procedural Knowledge in Datasets and Large Language Models
Declarative knowledge and procedural knowledge are two key parts in meta-cognitive theory, and these two hold significant importance in pre-training and inference of LLMs.
Academically intelligent LLMs are not necessarily socially intelligent
The academic intelligence of large language models (LLMs) has made remarkable progress in recent times, but their social intelligence performance remains unclear.
ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
As Large Language Models (LLMs) continue to advance in performance, their size has escalated significantly, with current LLMs containing billions or even trillions of parameters.
Learning or Self-aligning? Rethinking Instruction Fine-tuning
Instruction Fine-tuning~(IFT) is a critical phase in building large language models~(LLMs).
SoFA: Shielded On-the-fly Alignment via Priority Rule Following
The alignment problem in Large Language Models (LLMs) involves adapting them to the broad spectrum of human values.
Executing Natural Language-Described Algorithms with Large Language Models: An Investigation
In this paper, we seek to examine the capacity of present-day LLMs to comprehend and execute algorithms outlined in natural language.
Self-Retrieval: Building an Information Retrieval System with One Large Language Model
The rise of large language models (LLMs) has transformed the role of information retrieval (IR) systems in the way to humans accessing information.
Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models?
Building machines with commonsense has been a longstanding challenge in NLP due to the reporting bias of commonsense rules and the exposure bias of rule-based commonsense reasoning.
AI for social science and social science of AI: A Survey
Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possibilities.
DBCopilot: Scaling Natural Language Querying to Massive Databases
Text-to-SQL simplifies database interactions by enabling non-experts to convert their natural language (NL) questions into Structured Query Language (SQL) queries.
Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting
Incorporating factual knowledge in knowledge graph is regarded as a promising approach for mitigating the hallucination of large language models (LLMs).
Toward Unified Controllable Text Generation via Regular Expression Instruction
Controllable text generation is a fundamental aspect of natural language generation, with numerous methods proposed for different constraint types.
Benchmarking Large Language Models in Retrieval-Augmented Generation
In this paper, we systematically investigate the impact of Retrieval-Augmented Generation on large language models.
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
Existing approaches to tool learning have either primarily relied on extremely large language models, such as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or utilized supervised learning to train limited scopes of tools on compact models.
Learning In-context Learning for Named Entity Recognition
M}$, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i. e., $\mathcal{ (\lambda .
DLUE: Benchmarking Document Language Understanding
Understanding documents is central to many real-world tasks but remains a challenging topic.
Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models
Memory is one of the most essential cognitive functions serving as a repository of world knowledge and episodes of activities.
Harvesting Event Schemas from Large Language Models
Event schema provides a conceptual, structural and formal language to represent events and model the world event knowledge.
Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns
LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors.
ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models
(4) Can ChatGPT effectively leverage commonsense for answering questions?
The Life Cycle of Knowledge in Big Language Models: A Survey
Knowledge plays a critical role in artificial intelligence.
Universal Information Extraction as Unified Semantic Matching
Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching (USM) framework, which introduces three unified token linking operations to model the abilities of structuring and conceptualizing.
Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction
Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching.
Few-shot Named Entity Recognition with Self-describing Networks
In this paper, we propose a self-describing mechanism for few-shot NER, which can effectively leverage illustrative instances and precisely transfer knowledge from external resources by describing both entity types and mentions using a universal concept set.
Unified Structure Generation for Universal Information Extraction
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas.
Ranked #4 on
Aspect-Based Sentiment Analysis (ABSA)
on ASTE
(using extra training data)
Pre-training to Match for Unified Low-shot Relation Extraction
Low-shot relation extraction~(RE) aims to recognize novel relations with very few or even no samples, which is critical in real scenario application.
ECO v1: Towards Event-Centric Opinion Mining
Events are considered as the fundamental building blocks of the world.
Can Prompt Probe Pretrained Language Models? Understanding the Invisible Risks from a Causal View
Prompt-based probing has been widely used in evaluating the abilities of pretrained language models (PLMs).
Procedural Text Understanding via Scene-Wise Evolution
In this paper, we propose a new \textbf{scene-wise} paradigm for procedural text understanding, which jointly tracks states of all entities in a scene-by-scene manner.
Fine-grained Entity Typing via Label Reasoning
Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types.
Ranked #8 on
Entity Typing
on Open Entity
Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention
In this paper, we identify and solve the trigger curse problem in few-shot event detection (FSED) from a causal view.
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision
In this paper, we propose a new framework for unsupervised MRC.
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction
Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event.
Ranked #3 on
Event Extraction
on ACE2005
De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention
Distant supervision tackles the data bottleneck in NER by automatically generating training instances via dictionary matching.
Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model
Specifically, during neural network training, we naturally model the noise samples in each batch following a hypergeometric distribution parameterized by the noise-rate.
Element Intervention for Open Relation Extraction
Open relation extraction aims to cluster relation instances referring to the same underlying relation, which is a critical step for general relation extraction.
Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases
Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source.
From Discourse to Narrative: Knowledge Projection for Event Relation Extraction
Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events.
Syntactic and Semantic-driven Learning for Open Information Extraction
One of the biggest bottlenecks in building accurate, high coverage neural open IE systems is the need for large labelled corpora.
End-to-End Neural Event Coreference Resolution
Traditional event coreference systems usually rely on pipeline framework and hand-crafted features, which often face error propagation problem and have poor generalization ability.
ISCAS at SemEval-2020 Task 5: Pre-trained Transformers for Counterfactual Statement Modeling
ISCAS participated in two subtasks of SemEval 2020 Task 5: detecting counterfactual statements and detecting antecedent and consequence.
A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land?
Specifically, we erase name regularity, mention coverage and context diversity respectively from the benchmarks, in order to explore their impact on the generalization ability of models.
Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition
Current region-based NER models only rely on fully-annotated training data to learn effective region encoder, which often face the training data bottleneck.
Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning
Event detection systems rely on discrimination knowledge to distinguish ambiguous trigger words and generalization knowledge to detect unseen/sparse trigger words.
Cost-sensitive Regularization for Label Confusion-aware Event Detection
In supervised event detection, most of the mislabeling occurs between a small number of confusing type pairs, including trigger-NIL pairs and sibling sub-types of the same coarse type.
Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks
In this paper, we propose to resolve this problem by modeling and leveraging the head-driven phrase structures of entity mentions, i. e., although a mention can nest other mentions, they will not share the same head word.
Ranked #7 on
Nested Mention Recognition
on ACE 2005
Nugget Proposal Networks for Chinese Event Detection
Neural network based models commonly regard event detection as a word-wise classification task, which suffer from the mismatch problem between words and event triggers, especially in languages without natural word delimiters such as Chinese.
Adaptive Scaling for Sparse Detection in Information Extraction
This paper focuses on detection tasks in information extraction, where positive instances are sparsely distributed and models are usually evaluated using F-measure on positive classes.
Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension
Then we propose a multi-knowledge reasoning model, which selects inference rules for a specific reasoning context using attention mechanism, and reasons by summarizing all valid inference rules.
