I read an interesting and thought-provoking study by Negar Arabzadeh and co-authors arguing that MS MARCO official relevant documents tend to be inferior to top-documents returned by modern (BERT-based) neural rankers. This may indicate there was a bias in the original data set creation procedure. Some people rushed to conclude that this study rendered the IR leaderboards to be completely inadequate as a tool to measure progress. I believe this is an overstatement. Below I provide more detailed explanations. The paper (and the original variant of this blog post) caused lengthy Facebook and e-mail discussions, which convinced me to revise the blog post. Two particular points/questions were: (1) The crowd-sourcing procedure itself may introduce a bias, (2) Given a modest (~500) number of re-annotated queries, how likely is it to observe a similar shift in rankings due to a random selection of queries?
The study in question uses crowd-sourcing to re-evaluate several high performing runs (on the MS MARCO passage-ranking leaderboard) using a subset of about 500 MS MARCO queries. Authors asked crowd workers to directly compare all pairs of top documents returned by these top systems, which allowed them to create a new set of relevance judgements. In this crowd-sourcing experiment authors learned that when a neural ranker and MS MARCO relevance data disagreed on a top passage, the top result from the neural ranker was actually better in about 66% of the cases. They also found that when there was an agreement on the top passage between the MS MARCO data and the neural ranker, crowd-workers preferred that top passage to the second one in also about 66% of the cases, which is an important sanity check indicating a high/reasonable quality of the crowd-sourced data.
Missing important relevant answers is not necessarily a concern if the answers are missing completely at random, without creation of a pattern where system A consistently ranks missing relevant items higher than system B. In that, there is a disagreement on whether the study convincingly proves that such a pattern exists.
First, the study uses a tournament system to select best/preferred answers, which are uniformly better than other answers. This is not compatible with the original MS MARCO annotation strategy where there was no goal to identify the best answers. It may be the case that the new annotation procedure is itself biased. It will be, nevertheless, interesting to carefully examine the preferred relevant answers found by the tournament procedure to assess how they are different from documents that have a positive MS MARCO (relevance) label.
Second, the study uses a relatively small subset of MS MARCO queries (about 10% of the complete set), which can easily introduce substantial randomness. We previously found that small query sets were not reliable. In contrast, creators of MS MARCO showed there was a surprising stability of the ranking under bootstrapping (see Table 1 and 2) when one uses a fully set of queries (to sample from).
Let us now review some results that authors obtained with the crowd-sourced relevance judgements. Despite a shake-up of the ranking due to use of crowd-sourced judgements (instead of original MS MARCO labels) and a low correlation in before-after ranks, there is a clear trend (Figure 5) to rank recent top-performing leaderboard systems higher than older low-performing ones. One notable outlier is run L (RepBERT), which could have been much more effective than the leaderboard suggests. However, in the crowd-sourcing based re-evaluation L is still ranked lower than the top leaderboard run A. Furthermore, L has 0.1 lower MRR compared to runs B, C (and others) that perform best (or nearly so) in this crowd-sourcing experiment. These runs perform much better on the official leaderboard as well.
To conclude, I note that authors propose to set up a continuous evaluation procedure where a set of relevance judgements is reassessed as new runs are added. We all agree that it will be an interesting experiment to run. However, it may be difficult to pull off.