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Found 26 papers, 14 papers with code
RefuteBench 2.0 -- Agentic Benchmark for Dynamic Evaluation of LLM Responses to Refutation Instruction
Meta-evaluation shows that the LLM-based refuter could generate more human-like refutations and the evaluators could assign scores with high correlation with humans.
Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing
Dynamical systems theory provides a framework for analyzing iterative processes and evolution over time.
Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels
This study presents a comprehensive evaluation of GPT-4's translation capabilities compared to human translators of varying expertise levels.
Keys to Robust Edits: from Theoretical Insights to Practical Advances
Large language models (LLMs) have revolutionized knowledge storage and retrieval, but face challenges with conflicting and outdated information.
ELICIT: LLM Augmentation via External In-Context Capability
By externally storing and reusing vectors that represent in-context learned capabilities, \alg not only demonstrates the potential to operate modular capabilities but also significantly enhances the performance, versatility, adaptability, and scalability of large language models.
See What LLMs Cannot Answer: A Self-Challenge Framework for Uncovering LLM Weaknesses
We then build a benchmark, SC-G4, consisting of 1, 835 instances generated by GPT-4 using these patterns, with human-annotated gold responses.
GPT-4 vs. Human Translators: A Comprehensive Evaluation of Translation Quality Across Languages, Domains, and Expertise Levels
This study comprehensively evaluates the translation quality of Large Language Models (LLMs), specifically GPT-4, against human translators of varying expertise levels across multiple language pairs and domains.
What Have We Achieved on Non-autoregressive Translation?
Recent advances have made non-autoregressive (NAT) translation comparable to autoregressive methods (AT).
RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models
The application scope of large language models (LLMs) is increasingly expanding.
Potential and Challenges of Model Editing for Social Debiasing
Large language models (LLMs) trained on vast corpora suffer from inevitable stereotype biases.
Dynamics of Instruction Tuning: Each Ability of Large Language Models Has Its Own Growth Pace
Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models (LLMs).
Understanding In-Context Learning from Repetitions
This paper explores the elusive mechanism underpinning in-context learning in Large Language Models (LLMs).
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation
Additionally, based on the same intuition, we propose a 2-Step method, which takes both conversation and summary as input to simulate human annotation process.
Explicit Syntactic Guidance for Neural Text Generation
Most existing text generation models follow the sequence-to-sequence paradigm.
Non-Autoregressive Document-Level Machine Translation
However, current NAT models still have a significant performance gap compared to their AT counterparts.
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding
In this work, we show that the issue arises from the un-consistency of label smoothing on the token-level and sequence-level distributions.
Probing Causes of Hallucinations in Neural Machine Translations
Hallucination, one kind of pathological translations that bothers Neural Machine Translation, has recently drawn much attention.
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation
While large-scale neural language models, such as GPT2 and BART, have achieved impressive results on various text generation tasks, they tend to get stuck in undesirable sentence-level loops with maximization-based decoding algorithms (\textit{e. g.}, greedy search).
Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space
Current evaluation of an NMT system is usually built upon a heuristic decoding algorithm (e. g., beam search) and an evaluation metric assessing similarity between the translation and golden reference.
Selective Knowledge Distillation for Neural Machine Translation
As an active research field in NMT, knowledge distillation is widely applied to enhance the model's performance by transferring teacher model's knowledge on each training sample.
Multi-Unit Transformers for Neural Machine Translation
Transformer models achieve remarkable success in Neural Machine Translation.
A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph
Therefore, we propose a novel Sentiment-Controllable topic-to-essay generator with a Topic Knowledge Graph enhanced decoder, named SCTKG, which is based on the conditional variational autoencoder (CVAE) framework.
WeChat Neural Machine Translation Systems for WMT20
We participate in the WMT 2020 shared news translation task on Chinese to English.
Dual Past and Future for Neural Machine Translation
Though remarkable successes have been achieved by Neural Machine Translation (NMT) in recent years, it still suffers from the inadequate-translation problem.
Learning to Encode Evolutionary Knowledge for Automatic Commenting Long Novels
Static knowledge graph has been incorporated extensively into sequence-to-sequence framework for text generation.
Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision
This paper formulates the problem of relation extraction with temporal reasoning and proposes a solution to predict whether two given entities participate in a relation at a given time spot.
