Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models
Authors: Minghan Wang, Thuy-Trang Vu, Yuxia Wang, Ehsan Shareghi, Gholamreza Haffari


Reviewer paper summaries: This paper proposes Conversational Simultaneous MT to efficiently leverage LLM for simultaneous translation. It adopts a dialogue style prompt so that no recomputation of LLM is needed and develops a word-alignment based data synthesis method to generate trajectories for LLM finetuning. Experiments show that Conversational SimulMT is able to achieve similar quality with methods that requires LLM recomputation while significantly reduces the computation aware latency.
Summary of strengths: 1. The data synthesis method and the data augmentation method work well as shown in experiments. The ablation of data augmentation further reveals that the data augmentation makes LLM more robust to exposure bias. This is applicable to other SimulMT models. 
2. The dialogue style prompt reduces the latency of LLM based model (7B) to that of much smaller baseline models (0.3B). This is amazing.
Summary of weaknesses: 1. Clarification needed
   *  How to format a trajectory into the conversational format? Do you predict empty string for if you want to read next word?
   *  How to control the latency for greedy decoding?
2. Lack a few baselines
   *  Use offline prompt but don't recompute the states for target tokens that are already generated
   *  Direct beam search without RALCP
   *  Wait-K applied on pretrained NMT models and LLMs
   *  Other policies like Hidden Markov Transformer [1]

[1] https://arxiv.org/abs/2303.00257
Comments, suggestions and typos: 1. Other than baselines I mentioned above, I am also interested in how better LLMs like GPT-4o perform with few-shot dialogue style prompts.
2. Why connecting 2 -> 2 in figure 2 step 2? max(a_2) = 4 is already larger than max(a_1) = 2. 
3. Is fastalign reliable? Can it deal with phrase to phrase mapping? 
4. Is there a specific reason to apply cross entropy loss only on target tokens without shift operation? why no shift?
5. LAAL would be preferred.
6. Any explanation or analysis on why on COMET Conver. Prompt is better but on BLEU Conver. Prompt is worse in Figure 3?
7. In figure 5, without data augmentation, the BLEU score is so low. Is it because of exposure bias, i.e., large training inference mismatch?
Overall assessment scores: 3