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Found 18 papers, 9 papers with code
CA*: Addressing Evaluation Pitfalls in Computation-Aware Latency for Simultaneous Speech Translation
Simultaneous speech translation (SimulST) systems must balance translation quality with response time, making latency measurement crucial for evaluating their real-world performance.
Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling
To address these limitations, we introduce Speculative Knowledge Distillation (SKD), a novel approach that leverages cooperation between student and teacher models to generate high-quality training data on-the-fly while aligning with the student's inference-time distribution.
Uncovering Factor Level Preferences to Improve Human-Model Alignment
Our factor level analysis reveals a substantial discrepancy between human and LLM preferences in generation tasks, whereas LLMs show strong alignment with human preferences in evaluation tasks.
Translation Canvas: An Explainable Interface to Pinpoint and Analyze Translation Systems
With the rapid advancement of machine translation research, evaluation toolkits have become essential for benchmarking system progress.
BPO: Staying Close to the Behavior LLM Creates Better Online LLM Alignment
Direct alignment from preferences (DAP) has emerged as a promising paradigm for aligning large language models (LLMs) to human desiderata from pre-collected, offline preference datasets.
Pride and Prejudice: LLM Amplifies Self-Bias in Self-Refinement
We discovered that such a contrary is due to LLM's bias in evaluating their own output.
LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback
Recent large language models (LLM) are leveraging human feedback to improve their generation quality.
Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies
Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks.
INSTRUCTSCORE: Explainable Text Generation Evaluation with Finegrained Feedback
By harnessing both explicit human instruction and the implicit knowledge of GPT-4, we fine-tune a text evaluation metric based on LLaMA, producing both a score for generated text and a human readable diagnostic report.
CausalDialogue: Modeling Utterance-level Causality in Conversations
Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans.
SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes
In this paper, we propose SESCORE2, a self-supervised approach for training a model-based metric for text generation evaluation.
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis
Is it possible to build a general and automatic natural language generation (NLG) evaluation metric?
Visualize Before You Write: Imagination-Guided Open-Ended Text Generation
Recent advances in text-to-image synthesis make it possible to visualize machine imaginations for a given context.
Anticipating the Unseen Discrepancy for Vision and Language Navigation
In this paper, we propose an Unseen Discrepancy Anticipating Vision and Language Navigation (DAVIS) that learns to generalize to unseen environments via encouraging test-time visual consistency.
Neuro-Symbolic Procedural Planning with Commonsense Prompting
Procedural planning aims to implement complex high-level goals by decomposition into sequential simpler low-level steps.
PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers
Building natural language inference (NLI) benchmarks that are both challenging for modern techniques, and free from shortcut biases is difficult.
Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer
This is a particularly notable issue in the medical domain, where layman are often confused by medical text online.
Fake News Detection with Different Models
This is a paper for exploring various different models aiming at developing fake news detection models and we had used certain machine learning algorithms and we had used pretrained algorithms such as TFIDF and CV and W2V as features for processing textual data.
