7 accepted papers
Evaluating large language models (LLMs) for software engineering has been limited by narrow task coverage, language bias, and insufficient alignment with real-world developer workflows. Existing benchmarks often focus on algorithmic problems or Python-centric bug fixing, leaving critical dimensions …
Speculative decoding (SD) is a promising method for accelerating the decoding process of Large Language Models (LLMs). The efficiency of SD primarily hinges on the consistency between the draft model and the verify model. However, existing drafting approaches typically require additional modules to…
An important trend in the realm of large language models (LLMs) is the development of longer context windows. However, training LLMs with long context windows to acquire the capability of effectively modeling lengthy inputs is often hindered by the scarcity of naturally long-context data. Existing m…
Recently, large language models (LLMs) have emerged as a groundbreaking technology and their unparalleled text generation capabilities have sparked interest in their application to the fundamental sentence representation learning task. Existing methods have explored utilizing LLMs as data annotators…
Data augmentation (DA) methods have been proven to be effective for pre-trained language models (PLMs) in low-resource settings, including few-shot named entity recognition (NER). However, existing NER DA techniques either perform rule-based manipulations on words that break the semantic coherence o…
In this paper, a new robust position controller is proposed for Series Elastic Actuators (SEAs) by using Sliding Mode Control (SMC) and a second order Disturbance Observer (DOb). The latter estimates not only disturbances but also their first and second order successive derivatives. A simple yet eff…
This paper studies the dirty-paper coding (DPC) based secure transmission in a multiuser broadcast channel. Since the encoding order of DPC determines which information-bearing signals must be treated as noise by potential eavesdroppers, adopting DPC enables the accurate characterization of the intr…