Bump dependencies, add new work using Inseq to readme

Makefile

@@ -58,6 +58,7 @@ install-dev:
 #	-poetry run mypy --install-types --non-interactive ./
 	poetry run pre-commit install
 	poetry run pre-commit autoupdate
+	poetry self add poetry-plugin-export
 
 .PHONY: install-gpu
 install-gpu: install add-torch-gpu

README.md

@@ -316,12 +316,21 @@ Inseq has been used in various research projects. A list of known publications t
   <summary><b>2023</b></summary>
   <ol>
     <li> <a href="https://aclanthology.org/2023.acl-demo.40/">Inseq: An Interpretability Toolkit for Sequence Generation Models</a> (Sarti et al., 2023) </li>
-    <li> <a href="https://arxiv.org/abs/2302.14220">Are Character-level Translations Worth the Wait? Comparing Character- and Subword-level Models for Machine Translation</a> (Edman et al., 2023) </li>
+    <li> <a href="https://arxiv.org/abs/2302.14220">Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation</a> (Edman et al., 2023) </li>
     <li> <a href="https://aclanthology.org/2023.nlp4convai-1.1/">Response Generation in Longitudinal Dialogues: Which Knowledge Representation Helps?</a> (Mousavi et al., 2023)  </li>
-    <li> <a href="https://arxiv.org/abs/2310.01188">Quantifying the Plausibility of Context Reliance in Neural Machine Translation</a> (Sarti et al., 2023)</li>
-    <li> <a href="https://arxiv.org/abs/2310.12127">A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation</a> (Attanasio et al., 2023)</li>
+    <li> <a href="https://openreview.net/forum?id=XTHfNGI3zT">Quantifying the Plausibility of Context Reliance in Neural Machine Translation</a> (Sarti et al., 2023)</li>
+    <li> <a href="https://aclanthology.org/2023.emnlp-main.243/">A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation</a> (Attanasio et al., 2023)</li>
     <li> <a href="https://arxiv.org/abs/2310.09820">Assessing the Reliability of Large Language Model Knowledge</a> (Wang et al., 2023)</li>
     <li> <a href="https://aclanthology.org/2023.conll-1.18/">Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue</a> (Molnar et al., 2023)</li>
   </ol>
 
 </details>
+
+<details>
+  <summary><b>2023</b></summary>
+  <ol>
+    <li><a href="https://arxiv.org/abs/2401.12576">LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools</a> (Wang et al., 2024)</li>
+    <li><a href="https://arxiv.org/abs/2402.00794">ReAGent: A Model-agnostic Feature Attribution Method for Generative Language Models</a> (Zhao et al., 2024)</li>
+  </ol>
+
+</details>