readme fixes

README.md

@@ -1,20 +1,20 @@
 # Translation Agent: Agentic translation using reflection workflow
 
 This is a Python-based demonstration of a reflection agentic workflow for machine translation. The main steps are:
-1. Prompt an LLM to translate a text from {source_language} to {target_language}
-2. Reflect on the translation to come up with constructive suggestions for improving it 
-3. Use the suggestions to improve the translation
+1. Prompt an LLM to translate a text from `source_language` to `target_language`; 
+2. Reflect on the translation to come up with constructive suggestions for improving it; 
+3. Use the suggestions to improve the translation. 
 
 ## Customizability 
 
-By using an LLM as the heart of the translation engine, this system is highly steerable. For example, by changing the prompts, using this workflow an LLM is easier than a traditional machine translation (MT) system to:
+By using an LLM as the heart of the translation engine, this system is highly steerable. For example, by changing the prompts, using this workflow is easier than a traditional machine translation (MT) system to:
 - Modify the output's style, such as formal/informal.
-- Specify how to handle idioms and special terms like names, technical terms, and acronyms. For example, including a glossary in the prompt lets you make sure particular terms (such as open source, H100 and GPU) are translated consistently. 
+- Specify how to handle idioms and special terms like names, technical terms, and acronyms. For example, including a glossary in the prompt lets you make sure particular terms (such as 'open source', H100 and GPU) are translated consistently. 
 - Specific regional use of the language, or specific dialects, to serve a target audience. For example, Spanish spoken in Latin America is different from Spanish spoken in Spain; French spoken in Canada is different from how it is spoken in France. 
 
 **This is not mature software**, and is the result of Andrew playing around with translations on weekends the past few months, plus collaborators (Joaquin Dominguez, Nedelina Teneva, John Santerre) helping refactor the code. 
 
-According to our evaluations using BLEU score on traditional translation datasets, this workflow is sometimes competitive with, but also sometimes worse, than leading commercial offerings. However, we’ve also occasionally gotten fantastic results (superior to commercial offerings) with this approach. We think this is just a starting point for agentic translations, and that this is a promising direction for MT with significant headroom for further improvement, which is why we’re releasing this demonstration to encourage more discussion, experimentation, research and open-source contributions. 
+According to our evaluations using BLEU score on traditional translation datasets, this workflow is sometimes competitive with, but also sometimes worse than, leading commercial offerings. However, we’ve also occasionally gotten fantastic results (superior to commercial offerings) with this approach. We think this is just a starting point for agentic translations, and that this is a promising direction for MT with significant headroom for further improvement, which is why we’re releasing this demonstration to encourage more discussion, experimentation, research and open-source contributions. 
 
 If agentic translations can generate better results than traditional architectures (such as an end-to-end transformer that inputs a text and directly outputs a translation) -- which are often faster/cheaper to run than our approach here -- this also provides a mechanism to automatically generate training data (parallel text corpora) that can be used to further train and improve traditional algorithms. (See also [this article in The Batch](https://www.deeplearning.ai/the-batch/building-models-that-learn-from-themselves/) on using LLMs to generate training data.) 
 
@@ -57,7 +57,7 @@ for both commercial and non-commercial purposes.
 
 Here’re ideas we haven’t had time to experiment with but that we hope the open-source community will:
 - **Try other LLMs.** We prototyped this primarily using gpt-4-turbo. We would love for others to experiment with other LLMs as well as other hyperparameter choices and see if some do better than others for particular language pairs. 
-- **Glossary Creation.** What’s the best way to efficiently build a glossary -- perhaps using an LLM -- of the most important terms that we want translated consistently? For example, many businesses use specialized terms that are not widely used on the internet and that LLMs thus don’t know about, and there are also many terms that can be translated in multiple ways; for example,”open source” in Spanish can be “Código abierto” or “Fuente abierta”; both are fine, but it’d better to pick one and stick with it for a single document. 
+- **Glossary Creation.** What’s the best way to efficiently build a glossary -- perhaps using an LLM -- of the most important terms that we want translated consistently? For example, many businesses use specialized terms that are not widely used on the internet and that LLMs thus don’t know about, and there are also many terms that can be translated in multiple ways; for example, ”open source” in Spanish can be “Código abierto” or “Fuente abierta”; both are fine, but it’d better to pick one and stick with it for a single document. 
 - **Glossary Usage and Implementation.** Given a glossary, what’s the best way to include it in the prompt? 
 - **Evaluations on different languages.** How does its performance vary in different languages? Are there changes that make it work better for particular source or target languages? (Note that for very high levels of performance, which MT systems are approaching, we’re not sure if BLEU is a great metric.) Also, its performance on lower resource languages needs further study. 
 - **Error analysis.** We’ve found that specifying a language and a country/region (e.g., “Spanish as colloquially spoken in Mexico”) does a pretty good job for our applications. Where does the current approach fall short? We’re also particularly interested in understanding its performance on specialized topics (like law, medicine) or special types of text (like movie subtitles) to understand its limitations. 
@@ -66,8 +66,8 @@ Here’re ideas we haven’t had time to experiment with but that we hope the op
 ## Related work 
 
 A few academic research groups are also starting to look at LLM-based and agentic translation. We think it’s early days for this field!
-- ChatGPT MT: Competitive for High- (but not Low-) Resource Languages, Robinson et al. https://arxiv.org/pdf/2309.07423
-- Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts, Wu et al. https://arxiv.org/pdf/2405.11804
-- How to Design Translation Prompts for ChatGPT: An Empirical Study, Gao et al. https://arxiv.org/pdf/2304.02182v2
+- *ChatGPT MT: Competitive for High- (but not Low-) Resource Languages*, Robinson et al., https://arxiv.org/pdf/2309.07423
+- *Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts*, Wu et al., https://arxiv.org/pdf/2405.11804
+- *How to Design Translation Prompts for ChatGPT: An Empirical Study*, Gao et al., https://arxiv.org/pdf/2304.02182v2