Papers by Kaibo Liu

7 papers
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)

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Challenge: Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs).
Approach: They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories.
Outcome: The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks.
STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework (P19-1)

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Challenge: Simultaneous translation is notoriously dif- ficult due to word-order differences.
Approach: They propose a prefix-to-prefix framework that implicitly learns to anticipate in a single translation model.
Outcome: The proposed framework achieves low latency and reasonable qual- ity on 4 directions.
Simultaneous Translation Policies: From Fixed to Adaptive (2020.acl-main)

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Challenge: Adaptive policies can balance translation quality and latency based on context information . previous methods on obtaining adaptive policies rely on complicated training process .
Approach: They propose to obtain adaptive policies by a simple heuristic composition of fixed policies . they propose to use a heurism to obtain policies that can outperform fixed ones .
Outcome: Experiments on Chinese -> English and German -> english show that adaptive policies outperform fixed policies by up to 4 BLEU points for the same latency.
Opportunistic Decoding with Timely Correction for Simultaneous Translation (2020.acl-main)

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Challenge: Existing approaches to balancing translation quality and latency are either too aggressive or too conservative.
Approach: They propose an opportunistic decoding technique that always (over-)generates a certain mount of extra words at each step to keep the audience on track with the latest information.
Outcome: The proposed technique reduces latency and increases BLEU with no over-generating . it also corrects mistakes in the overgenerated words when observing more context .
Fluent and Low-latency Simultaneous Speech-to-Speech Translation with Self-adaptive Training (2020.findings-emnlp)

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Challenge: Current approaches to simultaneous speech-to-speech translation accumulate more and more latencies in later sentences when the speaker talks faster.
Approach: They propose a method which generates more fluent target speech latency than the baseline . they propose to use self-adaptive translation to adjust the length of translations to accommodate different source speech rates.
Outcome: Xiong et al., 2019) show that the proposed method generates more fluent target speech latency than baseline . authors say it provides more natural communication process than speech-to-text translation . xiong and colleagues say the proposed technique is more efficient than current approaches .
LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs (2025.acl-long)

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Challenge: TrickCatcher generates test cases that pass existing tests yet contain bugs . a recent study found that tricky bugs are not detected by test suites .
Approach: They propose an LLM-powered approach to generating test cases for uncovering bugs in plausible programs . they use a PUT and specification to generate program variants, an input generator and an Llm to construct test inputs .
Outcome: The proposed approach achieves recall, precision, and F1 scores that are 1.80, 2.65, and 1.66 . trickCatcher generates program variants based on the program under test and its specification .
Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework (2020.findings-emnlp)

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Challenge: Text-to-speech synthesis (TTS) has seen rapid progress in recent years, but still suffers from latencies.
Approach: They propose a neural incremental TTS approach that synthesizes speech in an online fashion, playing a segment of audio while generating the next.
Outcome: Experiments on English and Chinese TTS show that the proposed approach achieves similar speech naturalness compared to full sentence TTS, but with a constant (1-2 words) latency.

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