Search Results for author:
Found 35 papers, 21 papers with code
SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models
We introduce SelfCite, a novel self-supervised approach that aligns LLMs to generate high-quality, fine-grained, sentence-level citations for the statements in their generated responses.
Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback
Inspired by relevance feedback, ReDE-RF proposes to re-frame hypothetical document generation as a relevance estimation task, using an LLM to select which documents should be used for nearest neighbor search.
Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps
We find that a linear classifier based on these lookback ratio features is as effective as a richer detector that utilizes the entire hidden states of an LLM or a text-based entailment model.
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization
Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input.
A Large-Scale Evaluation of Speech Foundation Models
In this work, we establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the paradigm for speech.
Curiosity-driven Red-teaming for Large Language Models
To probe when an LLM generates unwanted content, the current paradigm is to recruit a \textit{red team} of human testers to design input prompts (i. e., test cases) that elicit undesirable responses from LLMs.
SpeechDPR: End-to-End Spoken Passage Retrieval for Open-Domain Spoken Question Answering
However, the real-world problem of Open-domain SQA (openSQA), in which the machine needs to first retrieve passages that possibly contain the answer from a spoken archive in addition, was never considered.
Audio-Visual Neural Syntax Acquisition
We study phrase structure induction from visually-grounded speech.
SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation
Existing watermarking algorithms are vulnerable to paraphrase attacks because of their token-level design.
Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning
How can we perform computations over natural language representations to solve tasks that require symbolic and numeric reasoning?
DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models
Despite their impressive capabilities, large language models (LLMs) are prone to hallucinations, i. e., generating content that deviates from facts seen during pretraining.
Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
In this paper we present MI-Zero, a simple and intuitive framework for unleashing the zero-shot transfer capabilities of contrastively aligned image and text models on gigapixel histopathology whole slide images, enabling multiple downstream diagnostic tasks to be carried out by pretrained encoders without requiring any additional labels.
Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS
We also show that even if two SEs have similar performance on STS benchmarks, they can have very different behavior on HEROS.
Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering
We propose EAR, a query Expansion And Reranking approach for improving passage retrieval, with the application to open-domain question answering.
SAIL: Search-Augmented Instruction Learning
Large language models (LLMs) have been significantly improved by instruction fine-tuning, but still lack transparency and the ability to utilize up-to-date knowledge and information.
Cleansing Jewel: A Neural Spelling Correction Model Built On Google OCR-ed Tibetan Manuscripts
Then, we implemented a Confidence Score mechanism into the Transformer architecture to perform spelling correction tasks.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Interpretable Unified Language Checking
Despite recent concerns about undesirable behaviors generated by large language models (LLMs), including non-factual, biased, and hateful language, we find LLMs are inherent multi-task language checkers based on their latent representations of natural and social knowledge.
C2KD: Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval
Inspired by the fact that English text-video retrieval outperforms other languages, we train a student model using input text in different languages to match the cross-modal predictions from teacher models using input text in English.
DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings.
Ranked #13 on
Semantic Textual Similarity
on STS16
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering
We empirically showed that DUAL yields results comparable to those obtained by cascading ASR and text QA model and robust to real-world data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
On the Interplay Between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis
Are end-to-end text-to-speech (TTS) models over-parametrized?
Mitigating Biases in Toxic Language Detection through Invariant Rationalization
Automatic detection of toxic language plays an essential role in protecting social media users, especially minority groups, from verbal abuse.
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition
We investigate the existence of sparse subnetworks in pre-trained speech SSL models that achieve even better low-resource ASR results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Meta-learning for downstream aware and agnostic pretraining
Neural network pretraining is gaining attention due to its outstanding performance in natural language processing applications.
Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation
We study the possibilities of building a non-autoregressive speech-to-text translation model using connectionist temporal classification (CTC), and use CTC-based automatic speech recognition as an auxiliary task to improve the performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
SUPERB: Speech processing Universal PERformance Benchmark
SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.
Semi-Supervised Spoken Language Understanding via Self-Supervised Speech and Language Model Pretraining
Much recent work on Spoken Language Understanding (SLU) is limited in at least one of three ways: models were trained on oracle text input and neglected ASR errors, models were trained to predict only intents without the slot values, or models were trained on a large amount of in-house data.
What makes multilingual BERT multilingual?
Recently, multilingual BERT works remarkably well on cross-lingual transfer tasks, superior to static non-contextualized word embeddings.
Looking for Clues of Language in Multilingual BERT to Improve Cross-lingual Generalization
Token embeddings in multilingual BERT (m-BERT) contain both language and semantic information.
Dual Inference for Improving Language Understanding and Generation
Natural language understanding (NLU) and Natural language generation (NLG) tasks hold a strong dual relationship, where NLU aims at predicting semantic labels based on natural language utterances and NLG does the opposite.
Lifelong Language Knowledge Distillation
It is challenging to perform lifelong language learning (LLL) on a stream of different tasks without any performance degradation comparing to the multi-task counterparts.
A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT
Recently, multilingual BERT works remarkably well on cross-lingual transfer tasks, superior to static non-contextualized word embeddings.
SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answering
In addition to the potential of end-to-end SQA, the SpeechBERT can also be considered for many other spoken language understanding tasks just as BERT for many text processing tasks.
Ranked #3 on
Spoken Language Understanding
on Spoken-SQuAD
Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation
We first build a pointer-generator baseline model for conclusion generation.
Robust Chinese Word Segmentation with Contextualized Word Representations
In recent years, after the neural-network-based method was proposed, the accuracy of the Chinese word segmentation task has made great progress.
