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Found 20 papers, 12 papers with code
Developing a Reliable, General-Purpose Hallucination Detection and Mitigation Service: Insights and Lessons Learned
Hallucination, a phenomenon where large language models (LLMs) produce output that is factually incorrect or unrelated to the input, is a major challenge for LLM applications that require accuracy and dependability.
xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token
This paper introduces xRAG, an innovative context compression method tailored for retrieval-augmented generation.
RTP-LX: Can LLMs Evaluate Toxicity in Multilingual Scenarios?
Large language models (LLMs) and small language models (SLMs) are being adopted at remarkable speed, although their safety still remains a serious concern.
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs).
On Meta-Prompting
Modern generative language models are capable of interpreting input strings as instructions, or prompts, and carry out tasks based on them.
SCALE: Synergized Collaboration of Asymmetric Language Translation Engines
In this paper, we introduce SCALE, a collaborative framework that connects compact Specialized Translation Models (STMs) and general-purpose Large Language Models (LLMs) as one unified translation engine.
A User-Centered Evaluation of Spanish Text Simplification
We present an evaluation of text simplification (TS) in Spanish for a production system, by means of two corpora focused in both complex-sentence and complex-word identification.
In-context Autoencoder for Context Compression in a Large Language Model
These promising results imply a novel perspective on the connection between working memory in cognitive science and representation learning in LLMs, revealing ICAE's significant implications in addressing the long context problem and suggesting further research in LLM context management.
Interactive Editing for Text Summarization
Summarizing lengthy documents is a common and essential task in our daily lives.
Joint Repetition Suppression and Content Moderation of Large Language Models
Natural language generation (NLG) is one of the most impactful fields in NLP, and recent years have witnessed its evolution brought about by large language models (LLMs).
An Evaluation on Large Language Model Outputs: Discourse and Memorization
We present an empirical evaluation of various outputs generated by nine of the most widely-available large language models (LLMs).
Semiparametric Language Models Are Scalable Continual Learners
We demonstrate that SeMem improves the scalability of semiparametric LMs for continual learning over streaming data in two ways: (1) data-wise scalability: as the model becomes stronger through continual learning, it will encounter fewer difficult cases that need to be memorized, causing the growth of the non-parametric memory to slow down over time rather than growing at a linear rate with the size of training data; (2) model-wise scalability: SeMem allows a larger model to memorize fewer samples than its smaller counterpart because it is rarer for a larger model to encounter incomprehensible cases, resulting in a non-parametric memory that does not scale linearly with model size.
Pay Attention to Your Tone: Introducing a New Dataset for Polite Language Rewrite
We introduce \textsc{PoliteRewrite} -- a dataset for polite language rewrite which is a novel sentence rewrite task.
Momentum Calibration for Text Generation
The input and output of most text generation tasks can be transformed to two sequences of tokens and they can be modeled using sequence-to-sequence learning modeling tools such as Transformers.
Ranked #3 on
Text Summarization
on SAMSum
Extensible Prompts for Language Models on Zero-shot Language Style Customization
We propose eXtensible Prompt (X-Prompt) for prompting a large language model (LLM) beyond natural language (NL).
Latent Prompt Tuning for Text Summarization
In this paper, we propose Lotus (shorthand for Latent Prompt Tuning for Summarization), which is a single model that can be applied in both controlled and uncontrolled (without control signals) modes.
Lossless Acceleration for Seq2seq Generation with Aggressive Decoding
We study lossless acceleration for seq2seq generation with a novel decoding algorithm -- Aggressive Decoding.
Abstractive Text Summarization
Grammatical Error Correction
+4
Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation
We propose Speculative Decoding (SpecDec), for the first time ever, to formally study exploiting the idea of speculative execution to accelerate autoregressive (AR) decoding.
EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation
We introduce EdgeFormer -- a parameter-efficient Transformer for on-device seq2seq generation under the strict computation and memory constraints.
